From c9e51a22a9ee4ac1eeeef040e68e63fb345c4e66 Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Fri, 17 Jan 2025 18:43:45 +0800 Subject: [PATCH 01/27] add data-bucket --- .../data-partitioning/auto-bucket.md | 184 ----------------- .../data-partitioning/manual-bucketing.md | 190 ++++++++++++++++-- static/images/table-desigin/hash-bucket.png | Bin 0 -> 634551 bytes static/images/table-desigin/random-bucket.png | Bin 0 -> 612860 bytes 4 files changed, 168 insertions(+), 206 deletions(-) delete mode 100644 i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/auto-bucket.md create mode 100644 static/images/table-desigin/hash-bucket.png create mode 100644 static/images/table-desigin/random-bucket.png diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/auto-bucket.md b/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/auto-bucket.md deleted file mode 100644 index ca5ecd76fcfd9..0000000000000 --- a/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/auto-bucket.md +++ /dev/null @@ -1,184 +0,0 @@ ---- -{ - "title": "自动分桶", - "language": "zh-CN" -} ---- - - - - -用户经常设置不合适的 bucket，导致各种问题，这里提供一种方式，来自动设置分桶数。当前只对 OLAP 表生效。 - -:::caution -注意：这个功能在被 CCR 同步时将会失效。如果这个表是被 CCR 复制而来的，即 PROPERTIES 中包含`is_being_synced = true`时，在`show create table`中会显示开启状态，但不会实际生效。当`is_being_synced`被设置为 `false` 时，这些功能将会恢复生效，但`is_being_synced`属性仅供 CCR 外围模块使用，在 CCR 同步的过程中不要手动设置。 -::: - -以往创建分桶时需要手动设定分桶数，而自动分桶推算功能是 Apache Doris 可以动态地推算分桶个数，使得分桶数始终保持在一个合适范围内，让用户不再操心桶数的细枝末节。首先说明一点，为了方便阐述该功能，该部分会将桶拆分为两个时期的桶，即初始分桶以及后续分桶；这里的初始和后续只是本文为了描述清楚该功能而采用的术语，Apache Doris 分桶本身没有初始和后续之分。从上文中创建分桶一节我们知道，BUCKET_DESC 非常简单，但是需要指定分桶个数；而在自动分桶推算功能上，BUCKET_DESC 的语法直接将分桶数改成"Auto"，并新增一个 Properties 配置即可： - -```sql --- 旧版本指定分桶个数的创建语法 -DISTRIBUTED BY HASH(site) BUCKETS 20 - --- 新版本使用自动分桶推算的创建语法 -DISTRIBUTED BY HASH(site) BUCKETS AUTO -properties("estimate_partition_size" = "2G") -``` - -新增的配置参数 estimate_partition_size 表示一个单分区的数据量。该参数是可选的，如果没有给出则 Doris 会将 estimate_partition_size 的默认值取为 10GB。从上文中已经得知，一个分桶在物理层面就是一个 Tablet，为了获得最好的性能，建议 Tablet 的大小在 1GB - 10GB 的范围内。 - -那么自动分桶推算是如何保证 Tablet 大小处于这个范围内的呢？ - -- 若是整体数据量较小，则分桶数不要设置过多 -- 若是整体数据量较大，则应使桶数跟总的磁盘块数相关，充分利用每台 BE 机器和每块磁盘的能力 - -:::tip -estimate_partition_size 属性不支持 alter 操作 -::: - -## 初始分桶推算 - -1. 先根据数据量得出一个桶数 N。首先使用 estimate_partition_size 的值除以 5（按文本格式存入 Doris 中有 5 比 1 的数据压缩比计算），得到的结果为： - - ```Plain - (, 100MB)，则取 N=1 - [100MB, 1GB)，则取 N=2 - [1GB, )，则每 GB 一个分桶 - ``` - -2. 根据 BE 节点数以及每个 BE 节点的磁盘容量，计算出桶数 M。 - - ```Plain - 其中每个 BE 节点算 1，每 50G 的磁盘容量算 1， - M 的计算规则为：M = BE 节点数 * ( 一块磁盘块大小 / 50GB) *磁盘块数 - 举例：有 3 台 BE，每台 BE 都有 4 块 500GB 的磁盘，那么 M = 3 (500GB / 50GB) 4 = 120 - ``` - -3. 得到最终的分桶个数计算逻辑： - - ```Plain - 先计算一个中间值 x = min(M, N, 128)， - 如果 x < N 并且 x < BE 节点个数，则最终分桶为 y 即 BE 节点个数； - 否则最终分桶数为 x - ``` - -4. x = max(x, autobucket_min_buckets), 这里 autobucket_min_buckets 是在 Config 中配置的，默认是 1 - - 上述过程伪代码表现形式为： - - ```Plain - int N = 计算 N 值; - int M = 计算 M 值; - - int y = BE 节点个数; - int x = min(M, N, 128); - - if (x < N && x < y) { - return y; - } - return x; - ``` - -5. 示例：有了上述算法，咱们再引入一些例子来更好地理解这部分逻辑。 - - ```Plain - case1: - 数据量 100 MB，10 台 BE 机器，2TB *3 块盘 - 数据量 N = 1 - BE 磁盘 M = 10* (2TB/50GB) * 3 = 1230 - x = min(M, N, 128) = 1 - 最终：1 - - case2: - 数据量 1GB, 3 台 BE 机器，500GB *2 块盘盘 - 数据量 N = 2 - BE 磁盘 M = 3* (500GB/50GB) * 2 = 60 - x = min(M, N, 128) = 2 - 最终：2 - - case3: - 数据量 100GB，3 台 BE 机器，500GB *2 块盘 - 数据量 N = 20 - BE 磁盘 M = 3* (500GB/50GB) * 2 = 60 - x = min(M, N, 128) = 20 - 最终：20 - - case4: - 数据量 500GB，3 台 BE 机器，1TB *1 块盘 - 数据量 N = 100 - BE 磁盘 M = 3* (1TB /50GB) * 1 = 6060 - x = min(M, N, 128) = 63 - 最终：63 - - case5: - 数据量 500GB，10 台 BE 机器，2TB *3 块盘*3 块盘 - 数据量 N = 100 - BE 磁盘 M = 10* (2TB / 50GB) * 3 = 1230 - x = min(M, N, 128) = 100 - 最终：100 - - case 6: - 数据量 1TB，10 台 BE 机器，2TB *3 块盘 - 数据量 N = 205 - BE 磁盘 M = 10* (2TB / 50GB) * 3 = 1230 - x = min(M, N, 128) = 128 - 最终：128 - - case 7: - 数据量 500GB，1 台 BE 机器，100TB *1 块盘 - 数据量 N = 100 - BE 磁盘 M = 1* (100TB / 50GB) * 1 = 2048 - x = min(M, N, 128) = 100 - 最终：100 - - case 8: - 数据量 1TB, 200 台 BE 机器，4TB *7 块盘 - 数据量 N = 205 - BE 磁盘 M = 200* (4TB / 50GB) * 7 = 114800 - x = min(M, N, 128) = 128 - 最终：200 - ``` - -## 后续分桶推算 - -上述是关于初始分桶的计算逻辑，后续分桶数因为已经有了一定的分区数据，可以根据已有的分区数据量来进行评估。后续分桶数会根据最多前 7 个分区数据量的 EMA（短期指数移动平均线）值，作为 estimate_partition_size 进行评估。此时计算分桶有两种计算方式，假设以天来分区，往前数第一天分区大小为 S7，往前数第二天分区大小为 S6，依次类推到 S1。 - -- 如果 7 天内的分区数据每日严格递增，则此时会取趋势值 - - 有 6 个 delta 值，分别是 - - ```Plain - S7 - S6 = delta1, - S6 - S5 = delta2, - ... - S2 - S1 = delta6 - ``` - - 由此得到 ema(delta) 值：那么，今天的 estimate_partition_size = S7 + ema(delta)。 - -- 非第一种的情况，此时直接取前几天的 EMA 平均值 - - 今天的 estimate_partition_size = EMA(S1, ..., S7)。 - -## 说明 - -根据上述算法，初始分桶个数以及后续分桶个数都能被计算出来。跟之前只能指定固定分桶数不同，由于业务数据的变化，有可能前面分区的分桶数和后面分区的分桶数不一样，这对用户是透明的，用户无需关心每一分区具体的分桶数是多少，而这一自动推算的功能会让分桶数更加合理。 - -开启 autobucket 之后，在`show create table`的时候看到的 schema 也是`BUCKETS AUTO`.如果想要查看确切的 bucket 数，可以通过`show partitions from ${table};`来查看。 diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/manual-bucketing.md b/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/manual-bucketing.md index 65c59bb707b4a..4e45de55e42fc 100644 --- a/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/manual-bucketing.md +++ b/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/manual-bucketing.md @@ -1,6 +1,6 @@ --- { - "title": "手动分桶", + "title": "数据分桶", "language": "zh-CN" } --- @@ -25,44 +25,190 @@ under the License. --> -如果使用了分区，则 `DISTRIBUTED ...` 语句描述的是数据在各个分区内的划分规则。 -如果不使用分区，则描述的是对整个表的数据的划分规则。 +一个分区可以进一步根据业务逻辑被划分为不同的数据分桶（bucket）。每个分桶会被存储为一个物理的数据分片（tablet）。一个合理的分桶策略能够有效减少查询过程中数据的扫描量，从而提升查询性能并增加查询并发能力。 -也可以对每个分区单独指定分桶方式。 +## 分桶方式 -分桶列可以是多列，Aggregate 和 Unique 模型必须为 Key 列，Duplicate 模型可以是 key 列和 value 列。分桶列可以和 Partition 列相同或不同。 +Doris 支持两种分桶方式：Hash 分桶与 Random 分桶。 -分桶列的选择，是在 查询吞吐 和 查询并发 之间的一种权衡： +### Hash 分桶 -- 如果选择多个分桶列，则数据分布更均匀。如果一个查询条件不包含所有分桶列的等值条件，那么该查询会触发所有分桶同时扫描，这样查询的吞吐会增加，单个查询的延迟随之降低。这个方式适合大吞吐低并发的查询场景。 +在创建表或新增分区时，用户需选择一列或多列作为分桶列，并明确指定分桶的数量。在同一分区内，系统会根据分桶键和分桶数量进行哈希计算。哈希值相同的数据会被分配到同一个分桶中。例如，在下图中，p250102 分区根据 region 列被划分为 3 个分桶，哈希值相同的行被归入同一个分桶。 -- 如果仅选择一个或少数分桶列，则对应的点查询可以仅触发一个分桶扫描。此时，当多个点查询并发时，这些查询有较大的概率分别触发不同的分桶扫描，各个查询之间的 IO 影响较小（尤其当不同桶分布在不同磁盘上时），所以这种方式适合高并发的点查询场景。 +![hash-bucket](images/table-desigin/hash-bucket.png) -## Bucket 的数量和数据量的建议 +在以下场景中，推荐使用 Hash 分桶： -- 一个表的 Tablet 总数量等于 (Partition num * Bucket num)。 +* 当业务需求频繁根据某个字段进行过滤时，可将该字段作为分桶键进行 Hash 分桶，以提高查询效率。 -- 一个表的 Tablet 数量，在不考虑扩容的情况下，推荐略多于整个集群的磁盘数量。 +* 当表中数据分布相对均匀时，Hash 分桶同样是一个合适的选择。 -- 单个 Tablet 的数据量理论上没有上下界，但建议在 1G - 10G 的范围内。如果单个 Tablet 数据量过小，则数据的聚合效果不佳，且元数据管理压力大。如果数据量过大，则不利于副本的迁移、补齐，且会增加 Schema Change 或者 Rollup 操作失败重试的代价（这些操作失败重试的粒度是 Tablet）。 +以下示例展示了如何创建带有 Hash 分桶的表。详细语法请参考 CREATE TABLE 语句。 -- 当 Tablet 的数据量原则和数量原则冲突时，建议优先考虑数据量原则。 +```sql +CREATE TABLE demo.hash_bucket_tbl( + oid BIGINT, + dt DATE, + region VARCHAR(10), + amount INT +) +DUPLICATE KEY(oid) +PARTITION BY RANGE(dt) ( + PARTITION p250101 VALUES LESS THAN("2025-01-01"), + PARTITION p250102 VALUES LESS THAN("2025-01-02") +) +DISTRIBUTED BY HASH(region) BUCKETS 8; +``` -- 在建表时，每个分区的 Bucket 数量统一指定。但是在动态增加分区时（`ADD PARTITION`），可以单独指定新分区的 Bucket 数量。可以利用这个功能方便的应对数据缩小或膨胀。 +示例中，通过 `DISTRIBUTED BY HASH(region)` 指定了创建 Hash 分桶，并选择 `region` 列作为分桶键。同时，通过 `BUCKETS 8` 指定了创建 8 个分桶。 -- 一个 Partition 的 Bucket 数量一旦指定，不可更改。所以在确定 Bucket 数量时，需要预先考虑集群扩容的情况。比如当前只有 3 台 host，每台 host 有 1 块盘。如果 Bucket 的数量只设置为 3 或更小，那么后期即使再增加机器，也不能提高并发度。 +### Random 分桶 -- 举一些例子：假设在有 10 台 BE，每台 BE 一块磁盘的情况下。如果一个表总大小为 500MB，则可以考虑 4-8 个分片。5GB：8-16 个分片。50GB：32 个分片。500GB：建议分区，每个分区大小在 50GB 左右，每个分区 16-32 个分片。5TB：建议分区，每个分区大小在 50GB 左右，每个分区 16-32 个分片。 +在每个分区中，使用 Random 分桶会随机地将数据分散到各个分桶中，不依赖于某个字段的 Hash 值进行数据划分。Random 分桶能够确保数据均匀分散，从而避免由于分桶键选择不当而引发的数据倾斜问题。 + +在导入数据时，单次导入作业的每个批次会被随机写入到一个 tablet 中，以此保证数据的均匀分布。例如，在一次操作中，8 个批次的数据被随机分配到 `p250102` 分区下的 3 个分桶中。 + +![random-bucket](/images/random-bucket.png) + +在使用 Random 分桶时，可以开启单分片导入模式（将 `load_to_single_tablet` 设置为 `true`）。在进行大规模数据导入时，一个批次的数据只会写入到一个数据分片，这有助于提高数据导入的并发度和吞吐量，减少因数据导入和 Compaction 导致的写放大问题，从而保障集群的稳定性。 + +在以下场景中，建议使用 Random 分桶： + +* 在任意维度分析的场景中，业务没有特别针对某一列频繁进行过滤或关联查询时，可以选择 Random 分桶； + +* 当经常查询的列或组合列数据分布极其不均匀时，使用 Random 分桶可以避免数据倾斜。 + +* Random 分桶无法根据分桶键进行剪裁，会扫描命中分区的所有数据，不建议在点查场景下使用； + +* 只有 DUPLICATE 表可以使用 Random 分区，UNIQUE 与 AGGREGATE 表无法使用 Random 分桶； + +以下示例展示了如何创建带有 Random 分桶的表。详细语法请参考 CREATE TABLE 语句： + +```sql +CREATE TABLE demo.random_bucket_tbl( + oid BIGINT, + dt DATE, + region VARCHAR(10), + amount INT +) +DUPLICATE KEY(oid) +PARTITION BY RANGE(dt) ( + PARTITION p250101 VALUES LESS THAN("2025-01-01"), + PARTITION p250102 VALUES LESS THAN("2025-01-02") +) +DISTRIBUTED BY RANDOM BUCKETS 8; +``` + +示例中，通过 `DISTRIBUTED BY RANDOM` 语句指定了使用 Random 分桶，创建 Random 分桶无需选择分桶键，通过 `BUCKETS 8` 语句指定创建 8 个分桶。 + +## 选择分桶键 + +:::tip 提示 + +只有 Hash 分桶需要选择分桶键，Random 分桶不需要选择分桶键。 -:::tip -表的数据量可以通过 `SHOW DATA` 命令查看（该命令的统计结果会有延迟），结果除以副本数，即表的数据量。 ::: -## Random Distribution +分桶键可以是一列或者多列。如果是 DUPLICATE 表，任何 Key 列与 Value 列都可以作为分桶键。如果是 AGGREGATE 或 UNIQUE 表，为了保证逐渐的聚合性，分桶列必须是 Key 列。 + +通常情况下，可以根据以下规则选择分桶键： + +* **利用查询过滤条件：**使用查询中的过滤条件进行 Hash 分桶，有助于数据的剪裁，减少数据扫描量； + +* **利用高基数列：**选择高基数（唯一值较多）的列进行 Hash 分桶，有助于数据均匀的分散在每一个分桶中； + +* **高并发点查场景：**建议选择单列或较少列进行分桶。点查可能仅触发一个分桶扫描，不同查询之间触发不同分桶扫描的概率较大，从而减小查询间的 IO 影响。 + +* **大吞吐查询场景：**建议选择多列进行分桶，使数据更均匀分布。若查询条件不能包含所有分桶键的等值条件，将增加查询吞吐，降低单个查询延迟。 + +## 选择分桶数量 + +在 Doris 中，一个 bucket 会被存储为一个物理文件（tablet）。一个表的 Tablet 数量等于 partition\_num（分区数）乘以 bucket\_num（分桶数）。一旦指定 Partition 的数量，便不可更改。 + +在确定 bucket 数量时，需预先考虑机器扩容情况。自 2.0 版本起，Doris 支持根据机器资源和集群信息自动设置分区中的分桶数。 + +### 手动设置分桶数 + +通过 `DISTRIBUTED` 语句可以指定分桶数量： + +```sql +-- Set hash bucket num to 8 +DISTRIBUTED BY HASH(region) BUCKETS 8 + +-- Set random bucket num to 8 +DISTRIBUTED BY RANDOM BUCKETS 8 +``` + +在决定分桶数量时，通常遵循数量与大小两个原则，当发生冲突时，优先考虑大小原则： + +* 大小原则：建议一个 tablet 的大小在 1-10G 范围内。过小的 tablet 可能导致聚合效果不佳，增加元数据管理压力；过大的 tablet 则不利于副本迁移、补齐，且会增加 Schema Change 操作的失败重试代价； + +* 数量原则：在不考虑扩容的情况下，一个表的 tablet 数量建议略多于整个集群的磁盘数量。 + +例如，假设有 10 台 BE 机器，每个 BE 一块磁盘，可以按照以下建议进行数据分桶： + +| 单表大小 | 建议分桶数量 | +| ----- | ----------------------------- | +| 500MB | 4-8 个分桶 | +| 5GB | 6-16 个分桶 | +| 50GB | 32 个分桶 | +| 500GB | 建议分区，每个分区 50GB，每个分区 16-32 个分桶 | +| 5TB | 建议分区，每个分区 50GB，每个分桶 16-32 个分桶 | + +::: tip 提示 + +表的数据量可以通过 `SHOW DATA` 命令查看。结果需要除以副本数，及表的数据量。 + +::: + +### 自动设置分桶数 + +自动推算分桶数功能会根据过去一段时间的分区大小，自动预测未来的分区大小，并据此确定分桶数量。 + +```sql +-- Set hash bucket auto +DISTRIBUTED BY HASH(region) BUCKETS AUTO +properties("estimate_partition_size" = "20G") + +-- Set random bucket auto +DISTRIBUTED BY HASH(region) BUCKETS AUTO +properties("estimate_partition_size" = "20G") +``` + +在创建分桶时，可以通过 `estimate_partition_size` 属性来调整前期估算的分区大小。此参数为可选设置，若未给出，Doris 将默认取值为 10GB。请注意，该参数与后期系统通过历史分区数据推算出的未来分区大小无关。 + +## 维护数据分桶 + +:::tip 提示 + +目前，Doris 仅支持修改新增分区的分桶数量，对于以下操作暂不支持： + +1. 不支持修改分桶类型 + +2. 不支持修改分桶键 + +3. 不支持修改已创建的分桶的分桶数量 + +::: + +在建表时，已通过 `DISTRIBUTED` 语句统一指定了每个分区的数量。为了应对数据增长或减少的情况，在动态增加分区时，可以单独指定新分区的分桶数量。以下示例展示了如何通过 `ALTER TABLE` 命令来修改新增分区的分桶数： + +```sql +-- Modify hash bucket table +ALTER TABLE demo.hash_bucket_tbl +ADD PARTITION p250103 VALUES LESS THAN("2025-01-03") +DISTRIBUTED BY HASH(region) BUCKETS 16; + +-- Modify random bucket table +ALTER TABLE demo.random_bucket_tbl +ADD PARTITION p250103 VALUES LESS THAN("2025-01-03") +DISTRIBUTED BY RANDOM BUCKETS 16; -- 如果 OLAP 表没有更新类型的字段，将表的数据分桶模式设置为 RANDOM，则可以避免严重的数据倾斜 (数据在导入表对应的分区的时候，单次导入作业每个 batch 的数据将随机选择一个 tablet 进行写入)。 +-- Modify dynamic partition table +ALTER TABLE demo.dynamic_partition_tbl +SET ("dynamic_partition.buckets"="16"); +``` -- 当表的分桶模式被设置为 RANDOM 时，因为没有分桶列，无法根据分桶列的值仅对几个分桶查询，对表进行查询的时候将对命中分区的全部分桶同时扫描，该设置适合对表数据整体的聚合查询分析而不适合高并发的点查询。 +在修改分桶数量后，可以通过 SHOW PARTITION 命令查看修改后的分桶数量。 -- 如果 OLAP 表的是 Random Distribution 的数据分布，那么在数据导入的时候可以设置单分片导入模式（将 `load_to_single_tablet` 设置为 true），那么在大数据量的导入的时候，一个任务在将数据写入对应的分区时将只写入一个分片，这样将能提高数据导入的并发度和吞吐量，减少数据导入和 Compaction 导致的写放大问题，保障集群的稳定性。 diff --git a/static/images/table-desigin/hash-bucket.png b/static/images/table-desigin/hash-bucket.png new file mode 100644 index 0000000000000000000000000000000000000000..8b0e5ccea9a83772a050a507b98fefcfe2811e1f GIT binary patch literal 634551 zcmeFacU)6h^e<||0w^ld1ymGN1Vnlb>L@mXihv+RM2Lv=5_&`hL=4=q4y$$&;kSqHQXJ<8E3BZo6(8yzIR{#il3Q~bI#hUe81nd_CCI6p{g4hxfxfi zSh4ZsiQmqzSiv5)Vg+OJ`nBM{+_v9V1wK~VomV}wBCc{fVa19aD^C7)_=1zcP@DRF z{W|v#W2+r@UkK2-U^c+DODiwR?^K?|IbqF0$3V5)B~5jWyJI>bCa|F@wuGGuNAmrr zS#)+Tz6_P51nb|xsYTxBeSK;RAI#@U#2tH2--{2AIQSTp?e?%EDiCvKD#m7eiW!SxGj(@+wRB|Kah?&SVbH zU;8}ZN@vq=cG`DwT$}raW?Np6JTu}ie;kz;)+_S}`C?U58-BSv+`_g0Qg2%_n@ZQJ zTR1Pn7sIT-+`HGHb5{RyFYwG~Sc7K5sI&R?^J9_qSo+4X*5xOy?IhjMs$rmFwq z+_Qf3=CG+vzuc+UFmh}T6P;gWf%@gKx^񗣜SuO`t+A9016QNmpe7R0R6m)=>_N~(mTBX{dA3|7oeX>@ALxnGghMl z9eM%!8O%awKtE%(r7BEkKtIDT=nUv*tVXTEbO!V@;Fr#Te#UA`Oq(vj{RH@>OK?9e zHP9uvpAop|65P*Njj9FECAgnaZRtIrpRw8!tEEeDbO~ruj!Eb)& z1B%J5i-ZUq!7O@6{%{Tb=7+wIXf{bY$0>hFzwiI%Pe8zBiT=f9Pal2{S*6hrs~J$8?Twk8_MJ+M z&~@EBYJc3u2bGn1W@77pTGjuX+xu`3OjZ?KQybCABYbLd^hoTq7kc#Vzz}ufW|NA{ zV;Vp7+4na)CkE0L^9UbY!37FsLjJgbrzf~4gH1o3Vg5#l_A~

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data-bucketing.md} (100%) diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/manual-bucketing.md b/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/data-bucketing.md similarity index 100% rename from i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/manual-bucketing.md rename to i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/data-bucketing.md From 0eb6ebdd1025b742173275ce600cfbaab8bba3a0 Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Fri, 17 Jan 2025 18:50:55 +0800 Subject: [PATCH 03/27] Update data-bucketing.md --- .../current/table-design/data-partitioning/data-bucketing.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/data-bucketing.md b/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/data-bucketing.md index 4e45de55e42fc..92caf3cb9840c 100644 --- a/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/data-bucketing.md +++ b/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/data-bucketing.md @@ -36,7 +36,7 @@ Doris 支持两种分桶方式：Hash 分桶与 Random 分桶。 在创建表或新增分区时，用户需选择一列或多列作为分桶列，并明确指定分桶的数量。在同一分区内，系统会根据分桶键和分桶数量进行哈希计算。哈希值相同的数据会被分配到同一个分桶中。例如，在下图中，p250102 分区根据 region 列被划分为 3 个分桶，哈希值相同的行被归入同一个分桶。 -![hash-bucket](images/table-desigin/hash-bucket.png) +![hash-bucket](/images/table-desigin/hash-bucket.png) 在以下场景中，推荐使用 Hash 分桶： @@ -104,7 +104,7 @@ DISTRIBUTED BY RANDOM BUCKETS 8; ## 选择分桶键 -:::tip 提示 +:::tip提示 只有 Hash 分桶需要选择分桶键，Random 分桶不需要选择分桶键。 From 3baddad69d87d9d6d4aa795cab095a8fd243fb71 Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Fri, 17 Jan 2025 18:51:27 +0800 Subject: [PATCH 04/27] Update data-bucketing.md --- .../current/table-design/data-partitioning/data-bucketing.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/data-bucketing.md b/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/data-bucketing.md index 92caf3cb9840c..ea4b0b3a048c3 100644 --- a/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/data-bucketing.md +++ b/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/data-bucketing.md @@ -104,7 +104,7 @@ DISTRIBUTED BY RANDOM BUCKETS 8; ## 选择分桶键 -:::tip提示 +:::tip 提示 只有 Hash 分桶需要选择分桶键，Random 分桶不需要选择分桶键。 @@ -156,7 +156,7 @@ DISTRIBUTED BY RANDOM BUCKETS 8 | 500GB | 建议分区，每个分区 50GB，每个分区 16-32 个分桶 | | 5TB | 建议分区，每个分区 50GB，每个分桶 16-32 个分桶 | -::: tip 提示 +:::tip 提示 表的数据量可以通过 `SHOW DATA` 命令查看。结果需要除以副本数，及表的数据量。 From 18839050fa59df5e6a6bf4f03aaecb9746c98832 Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Fri, 17 Jan 2025 19:24:25 +0800 Subject: [PATCH 05/27] 2.1 3.0 en cn --- .../data-partitioning/auto-bucket.md | 195 ---------------- .../data-partitioning/data-bucketing.md | 215 ++++++++++++++++++ .../data-partitioning/manual-bucketing.md | 58 ----- .../data-partitioning/auto-bucket.md | 184 --------------- .../data-partitioning/data-bucketing.md | 214 +++++++++++++++++ .../data-partitioning/manual-bucketing.md | 68 ------ .../data-partitioning/auto-bucket.md | 184 --------------- .../data-partitioning/data-bucketing.md | 214 +++++++++++++++++ .../data-partitioning/manual-bucketing.md | 68 ------ sidebars.json | 3 +- .../data-partitioning/data-bucketing.md | 215 ++++++++++++++++++ .../data-partitioning/data-bucketing.md | 215 ++++++++++++++++++ versioned_sidebars/version-2.1-sidebars.json | 3 +- versioned_sidebars/version-3.0-sidebars.json | 3 +- 14 files changed, 1076 insertions(+), 763 deletions(-) delete mode 100644 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versioned_docs/version-2.1/table-design/data-partitioning/data-bucketing.md create mode 100644 versioned_docs/version-3.0/table-design/data-partitioning/data-bucketing.md diff --git a/docs/table-design/data-partitioning/auto-bucket.md b/docs/table-design/data-partitioning/auto-bucket.md deleted file mode 100644 index 91d47d59fb96e..0000000000000 --- a/docs/table-design/data-partitioning/auto-bucket.md +++ /dev/null @@ -1,195 +0,0 @@ ---- -{ - "title": "Auto bucket", - "language": "en" -} ---- - - - - -Users often encounter various issues due to improper bucket settings. To address this, we provide an automated approach for setting the number of buckets, which is currently applicable only to OLAP tables. - -:::tip - -This feature will be disabled when synchronized by CCR. If this table is copied by CCR, that is, PROPERTIES contains `is_being_synced = true`, it will be displayed as enabled in show create table, but will not actually take effect. When `is_being_synced` is set to `false`, these features will resume working, but the `is_being_synced` property is for CCR peripheral modules only and should not be manually set during CCR synchronization. - -::: - -In the past, user had to set the number of buckets manually when creating table, but the automatic bucket feature is a way for Apache Doris to dynamically project the number of buckets, so that the number of buckets always stays within a suitable range and users don't have to worry about the minutiae of the number of buckets. - -For the sake of clarity, this section splits the bucket into two periods, the initial bucket and the subsequent bucket; the initial and subsequent are just terms used in this article to describe the feature clearly, there is no initial or subsequent Apache Doris bucket. - -As we know from the section above on creating buckets, `BUCKET_DESC` is very simple, but you need to specify the number of buckets; for the automatic bucket projection feature, the syntax of BUCKET_DESC directly changes the number of buckets to `Auto` and adds a new Properties configuration. - -```sql --- old version of the creation syntax for specifying the number of buckets -DISTRIBUTED BY HASH(site) BUCKETS 20 - --- Newer versions use the creation syntax for automatic bucket imputation -DISTRIBUTED BY HASH(site) BUCKETS AUTO -properties("estimate_partition_size" = "100G") -``` - -The new configuration parameter estimate_partition_size indicates the amount of data for a single partition. This parameter is optional and if not given, Doris will take the default value of estimate_partition_size to 10GB. - -As you know from the above, a partitioned bucket is a tablet at the physical level, and for best performance, it is recommended that the tablet size be in the range of 1GB - 10GB. So how does the automatic bucketing projection ensure that the tablet size is within this range? - -To summarize, there are a few principles. - -- If the overall data volume is small, the number of buckets should not be set too high -- If the overall data volume is large, the number of buckets should be related to the total number of disk blocks, so as to fully utilize the capacity of each BE machine and each disk - -:::tip -propertie estimate_partition_size not support alter -::: - -## Initial bucketing projection - -1. Obtain a number of buckets N based on the data size. Initially, we divide the value of `estimate_partition_size` by 5 (considering a data compression ratio of 5 to 1 when storing data in text format in Doris). The result obtained is - -``` -(, 100MB), then take N=1 - -[100MB, 1GB), then take N=2 - -(1GB, ), then one bucket per GB -``` - -2. calculate the number of buckets M based on the number of BE nodes and the disk capacity of each BE node. - -``` -Where each BE node counts as 1, and every 50G of disk capacity counts as 1. - -The calculation rule for M is: M = Number of BE nodes * (Size of one disk block / 50GB) * Number of disk blocks. - -For example: If there are 3 BEs, and each BE has 4 disks of 500GB, then M = 3 * (500GB / 50GB) * 4 = 120. - -``` - -3. Calculation logic to get the final number of buckets. - -``` -Calculate an intermediate value x = min(M, N, 128). - -If x < N and x < the number of BE nodes, the final bucket is y. - -The number of BE nodes; otherwise, the final bucket is x. -``` - -4. x = max(x, autobucket_min_buckets), Here autobucket_min_buckets is configured in Config (where, default is 1) - -The pseudo-code representation of the above process is as follows - -``` -int N = Compute the N value; -int M = compute M value; - -int y = number of BE nodes; -int x = min(M, N, 128); - -if (x < N && x < y) { - return y; -} -return x; -``` - -With the above algorithm in mind, let's introduce some examples to better understand this part of the logic. - -``` -case1: -Amount of data 100 MB, 10 BE machines, 2TB * 3 disks -Amount of data N = 1 -BE disks M = 10* (2TB/50GB) * 3 = 1230 -x = min(M, N, 128) = 1 -Final: 1 - -case2: -Data volume 1GB, 3 BE machines, 500GB * 2 disks -Amount of data N = 2 -BE disks M = 3* (500GB/50GB) * 2 = 60 -x = min(M, N, 128) = 2 -Final: 2 - -case3: -Data volume 100GB, 3 BE machines, 500GB * 2 disks -Amount of data N = 20 -BE disks M = 3* (500GB/50GB) * 2 = 60 -x = min(M, N, 128) = 20 -Final: 20 - -case4: -Data volume 500GB, 3 BE machines, 1TB * 1 disk -Data volume N = 100 -BE disks M = 3* (1TB /50GB) * 1 = 60 -x = min(M, N, 128) = 63 -Final: 63 - -case5: -Data volume 500GB, 10 BE machines, 2TB * 3 disks -Amount of data N = 100 -BE disks M = 10* (2TB / 50GB) * 3 = 1230 -x = min(M, N, 128) = 100 -Final: 100 - -case 6: -Data volume 1TB, 10 BE machines, 2TB * 3 disks -Amount of data N = 205 -BE disks M = 10* (2TB / 50GB) * 3 = 1230 -x = min(M, N, 128) = 128 -Final: 128 - -case 7: -Data volume 500GB, 1 BE machine, 100TB * 1 disk -Amount of data N = 100 -BE disk M = 1* (100TB / 50GB) * 1 = 2048 -x = min(M, N, 128) = 100 -Final: 100 - -case 8: -Data volume 1TB, 200 BE machines, 4TB * 7 disks -Amount of data N = 205 -BE disks M = 200* (4TB / 50GB) * 7 = 114800 -x = min(M, N, 128) = 128 -Final: 200 -``` - -## Subsequent bucketing projection - -The above is the calculation logic for the initial bucketing. The subsequent bucketing can be evaluated based on the amount of partition data available since there is already a certain amount of partition data. The subsequent bucket size is evaluated based on the EMA[1] (short term exponential moving average) value of up to the first 7 partitions, which is used as the estimate_partition_size. At this point there are two ways to calculate the partition buckets, assuming partitioning by days, counting forward to the first day partition size of S7, counting forward to the second day partition size of S6, and so on to S1. - -- If the partition data in 7 days is strictly increasing daily, then the trend value will be taken at this time. There are 6 delta values, which are - -``` -S7 - S6 = delta1, -S6 - S5 = delta2, -... -S2 - S1 = delta6 -``` - -This yields the ema(delta) value.Then, today's estimate_partition_size = S7 + ema(delta) - -- not the first case, this time directly take the average of the previous days EMA. Today's estimate_partition_size = EMA(S1, ... , S7) , S7) - -:::tip - -According to the above algorithm, the initial number of buckets and the number of subsequent buckets can be calculated. Unlike before when only a fixed number of buckets could be specified, due to changes in business data, it is possible that the number of buckets in the previous partition is different from the number of buckets in the next partition, which is transparent to the user, and the user does not need to care about the exact number of buckets in each partition, and this automatic extrapolation will make the number of buckets more reasonable. - -::: diff --git a/docs/table-design/data-partitioning/data-bucketing.md b/docs/table-design/data-partitioning/data-bucketing.md new file mode 100644 index 0000000000000..8d249a9e0257e --- /dev/null +++ b/docs/table-design/data-partitioning/data-bucketing.md @@ -0,0 +1,215 @@ +--- +{ + "title": "数据分桶", + "language": "zh-CN" +} +--- + + + + + +A partition can further be divided into different data buckets based on business logic. Each bucket will be stored as a physical data tablet. A reasonable bucket strategy can effectively reduce the amount of data scanned during queries, thereby improving query performance and increasing query concurrency. + +## Bucket Methods + +Doris supports two bucket methods: Hash Bucketing and Random Bucketing. + +### Hash Bucketing + +When creating a table or adding a partition, users need to select one or more columns as the bucket columns and specify the number of buckets. Within the same partition, the system performs a hash calculation based on the bucket key and the number of buckets. Data with the same hash value will be allocated to the same bucket. For example, in the figure below, partition p250102 is divided into three buckets based on the region column, and rows with the same hash value are placed into the same bucket. + +![hash-bucket](/images/table-desigin/hash-bucket.png) + +It is recommended to use Hash Bucketing in the following scenarios: + +* When the business needs to frequently filter based on a certain field, you can use this field as the bucket key for Hash Bucketing to improve query efficiency. + +* When the data distribution in the table is relatively uniform, Hash Bucketing is also a suitable choice. + +The following example shows how to create a table with Hash Bucketing. For detailed syntax, please refer to the CREATE TABLE statement. + +```sql +CREATE TABLE demo.hash_bucket_tbl( + oid BIGINT, + dt DATE, + region VARCHAR(10), + amount INT +) +DUPLICATE KEY(oid) +PARTITION BY RANGE(dt) ( + PARTITION p250101 VALUES LESS THAN("2025-01-01"), + PARTITION p250102 VALUES LESS THAN("2025-01-02") +) +DISTRIBUTED BY HASH(region) BUCKETS 8; +``` + +In the example, `DISTRIBUTED BY HASH(region)` specifies the creation of Hash Bucketing and selects the `region` column as the bucket key. Meanwhile, `BUCKETS 8` specifies the creation of 8 buckets. + + +### Random Bucketing + +In each partition, Random Bucketing randomly distributes data to various buckets without relying on the hash value of a certain field. Random Bucketing ensures uniform data distribution, thus avoiding data skew caused by improper bucket key selection. + +During data import, each batch of a single import job will be randomly written to a tablet, ensuring uniform data distribution. For example, in one operation, eight batches of data are randomly allocated to three buckets under partition `p250102`. + +![random-bucket](/images/random-bucket.png) + +When using Random Bucketing, you can enable the single-tablet import mode (set `load_to_single_tablet` to `true`). During large-scale data imports, one batch of data will only be written to one data tablet, helping to improve the concurrency and throughput of data imports, reducing write amplification caused by data imports and Compaction, thereby ensuring cluster stability. + +It is recommended to use Random Bucketing in the following scenarios: + +* In scenarios of arbitrary dimension analysis, where the business does not frequently filter or join queries based on a specific column, you can choose Random Bucketing. + +* When the data distribution of frequently queried columns or combinations of columns is extremely uneven, using Random Bucketing can avoid data skew. + +* Random Bucketing cannot be pruned based on bucket keys and will scan all data in the hit partition, so it is not recommended for point query scenarios. + +* Only DUPLICATE tables can use Random partitioning. UNIQUE and AGGREGATE tables cannot use Random Bucketing. + +The following example shows how to create a table with Random Bucketing. For detailed syntax, please refer to the CREATE TABLE statement: + +```sql +CREATE TABLE demo.random_bucket_tbl( + oid BIGINT, + dt DATE, + region VARCHAR(10), + amount INT +) +DUPLICATE KEY(oid) +PARTITION BY RANGE(dt) ( + PARTITION p250101 VALUES LESS THAN("2025-01-01"), + PARTITION p250102 VALUES LESS THAN("2025-01-02") +) +DISTRIBUTED BY RANDOM BUCKETS 8; +``` + +In the example, the statement `DISTRIBUTED BY RANDOM` specifies the use of Random Bucketing. Creating Random Bucketing does not require selecting a bucket key, and the statement `BUCKETS 8` specifies the creation of 8 buckets. + +## Choosing Bucket Keys + +:::tip Note + +Only Hash Bucketing requires selecting bucket keys; Random Bucketing does not require selecting bucket keys. + +::: + +Bucket keys can be one or more columns. If it is a DUPLICATE table, any Key column or Value column can be used as the bucket key. If it is an AGGREGATE or UNIQUE table, to ensure gradual aggregation, the bucket column must be a Key column. + +Generally, you can choose bucket keys based on the following rules: + +* **Using Query Filter Conditions:** Using query filter conditions for Hash Bucketing helps data pruning and reduces data scan volume; + +* **Using High Cardinality Columns:** Selecting high cardinality (many unique values) columns for Hash Bucketing helps to evenly distribute data across each bucket; + +* **High-Concurrency Point Query Scenarios:** It is recommended to select a single column or fewer columns for bucketing. Point queries may only trigger a scan of one bucket, and the probability of different queries triggering scans of different buckets is high, thereby reducing the IO impact between queries. + +* **High-Throughput Query Scenarios:** It is recommended to select multiple columns for bucketing to make data more evenly distributed. If the query conditions cannot include the equality conditions of all bucket keys, it will increase query throughput and reduce the latency of single queries. + +## Choosing the Number of Buckets + +In Doris, a bucket is stored as a physical file (tablet). The number of tablets in a table equals `partition_num` (number of partitions) multiplied by `bucket_num` (number of buckets). Once the number of Partitions is specified, it cannot be changed. + +When determining the number of buckets, you need to consider machine expansion in advance. Starting from version 2.0, Doris supports automatically setting the number of buckets in partitions based on machine resources and cluster information. + +### Manually Setting the Number of Buckets + +You can specify the number of buckets using the `DISTRIBUTED` statement: + +```sql +-- Set hash bucket num to 8 +DISTRIBUTED BY HASH(region) BUCKETS 8 + +-- Set random bucket num to 8 +DISTRIBUTED BY RANDOM BUCKETS 8 +``` + +When determining the number of buckets, two principles are usually followed: quantity and size. If there is a conflict between the two, the size principle is prioritized: + +* **Size Principle:** It is recommended that the size of a tablet should be within the range of 1-10GB. Too small a tablet may result in poor aggregation effect and increase metadata management pressure; too large a tablet is not conducive to replica migration and supplementation and will increase the cost of retrying Schema Change operations. + +* **Quantity Principle:** Without considering expansion, it is recommended that the number of tablets for a table be slightly more than the number of disks in the entire cluster. + +For example, assuming there are 10 BE machines with one disk per BE, you can follow the recommendations below for data bucketing: + +| Table Size | Recommended Number of Buckets | +| ---------- | -------------------------------------- | +| 500MB | 4-8 buckets | +| 5GB | 6-16 buckets | +| 50GB | 32 buckets | +| 500GB | Partition recommended, 50GB per partition, 16-32 buckets per partition | +| 5TB | Partition recommended, 50GB per partition, 16-32 buckets per partition | + +:::tip Note + +The data volume of the table can be viewed using the `SHOW DATA` command. The result needs to be divided by the number of replicas and the data volume of the table. + +::: + +### Automatic Bucket Number Setting + +The automatic bucket number calculation function will automatically predict the future partition size based on the partition size over a period of time, and determine the number of buckets accordingly. + + +```sql +-- Set hash bucket auto +DISTRIBUTED BY HASH(region) BUCKETS AUTO +properties("estimate_partition_size" = "20G") + +-- Set random bucket auto +DISTRIBUTED BY HASH(region) BUCKETS AUTO +properties("estimate_partition_size" = "20G") +``` + +When creating buckets, you can adjust the estimated partition size through the `estimate_partition_size` attribute. This parameter is optional, and if not provided, Doris will default to 10GB. Please note that this parameter is not related to the future partition size calculated by the system based on historical partition data. + +## Maintaining Data Buckets + +:::tip Note + +Currently, Doris only supports modifying the number of buckets in newly added partitions and does not support the following operations: + +1. Modifying the bucketing type +2. Modifying the bucket key +3. Modifying the number of buckets for existing buckets + +::: + +When creating a table, the number of buckets for each partition is uniformly specified through the `DISTRIBUTED` statement. To cope with data growth or reduction, you can specify the number of buckets for the new partition when dynamically adding partitions. The following example shows how to modify the number of buckets in newly added partitions using the `ALTER TABLE` command: + + +```sql +-- Modify hash bucket table +ALTER TABLE demo.hash_bucket_tbl +ADD PARTITION p250103 VALUES LESS THAN("2025-01-03") +DISTRIBUTED BY HASH(region) BUCKETS 16; + +-- Modify random bucket table +ALTER TABLE demo.random_bucket_tbl +ADD PARTITION p250103 VALUES LESS THAN("2025-01-03") +DISTRIBUTED BY RANDOM BUCKETS 16; + +-- Modify dynamic partition table +ALTER TABLE demo.dynamic_partition_tbl +SET ("dynamic_partition.buckets"="16"); +``` + +在修改分桶数量后，可以通过 SHOW PARTITION 命令查看修改后的分桶数量。 + diff --git a/docs/table-design/data-partitioning/manual-bucketing.md b/docs/table-design/data-partitioning/manual-bucketing.md deleted file mode 100644 index 2eb67809b10df..0000000000000 --- a/docs/table-design/data-partitioning/manual-bucketing.md +++ /dev/null @@ -1,58 +0,0 @@ ---- -{ - "title": "Manual bucketing", - "language": "en" -} ---- - - - - -If partitions are used, `DISTRIBUTED ..`. statement describes the rules for dividing data within each partition. - -If partitions are not used, it describes the rules for dividing the data across the entire table. - -It is also possible to specify a bucketing method for each partition individually. - -The bucket columns can be multiple columns. For the Aggregate and Unique models, they must be Key columns, while for the duplicate key data model, they can be both key and value columns. Bucket columns can be the same as or different from Partition columns. - -The choice of bucket columns involves a trade-off between query throughput and query concurrency: - -- If multiple bucket columns are selected, the data distribution will be more uniform. If a query condition does not include equal conditions for all bucket columns, the query will trigger simultaneous scanning of all buckets, increasing query throughput and reducing the latency of individual queries. This approach is suitable for high-throughput, low-concurrency query scenarios. -- If only one or a few bucket columns are selected, a point query can trigger scanning of just one bucket. In this case, when multiple point queries are concurrent, there is a higher probability that they will trigger scanning of different buckets, reducing the IO impact between queries (especially when different buckets are distributed across different disks). Therefore, this approach is suitable for high-concurrency point query scenarios. - -## Recommendations for bucket number and data volume: - -- The total number of tablets for a table is equal to (Partition num * Bucket num). -- Without considering expansion, it is recommended that the number of tablets for a table be slightly more than the total number of disks in the cluster. -- In theory, there is no upper or lower limit for the data volume of a single tablet, but it is recommended to be within the range of 1G - 10G. If the data volume of a single tablet is too small, the data aggregation effect will not be good, and the metadata management pressure will be high. If the data volume is too large, it will not be conducive to the migration and replenishment of replicas, and it will increase the cost of retrying failed operations such as Schema Change or Rollup (the granularity of retrying these operations is the tablet). -- When there is a conflict between the data volume principle and the quantity principle of tablets, it is recommended to prioritize the data volume principle. -- When creating a table, the bucket number for each partition is uniformly specified. However, when dynamically adding partitions `ADD PARTITION`, the bucket number for the new partition can be specified separately. This feature can be conveniently used to handle data reduction or expansion. -- Once the bucket number for a partition is specified, it cannot be changed. Therefore, when determining the bucket number, it is necessary to consider the cluster expansion scenario in advance. For example, if there are only 3 hosts with 1 disk each, and the bucket number is set to 3 or less, then even if more machines are added later, the concurrency cannot be improved. - -Here are some examples: Assuming there are 10 BEs, each with one disk. If a table has a total size of 500MB, 4-8 tablets can be considered. For 5GB: 8-16 tablets. For 50GB: 32 tablets. For 500GB: It is recommended to partition the table, with each partition size around 50GB and 16-32 tablets per partition. For 5TB: It is recommended to partition the table, with each partition size around 50GB and 16-32 tablets per partition. - -The data volume of a table can be viewed using the [SHOW DATA](../../sql-manual/sql-statements/Show-Statements/SHOW-DATA) command, and the result should be divided by the number of replicas to obtain the actual data volume of the table. - -## Random distribution - -- If an OLAP table does not have fields of the update type, setting the data bucketing mode of the table to RANDOM can avoid severe data skew. When data is imported into the corresponding partitions of the table, each batch of a single import job will randomly select a tablet for writing. -- When the bucketing mode of a table is set to RANDOM, there is no bucketing column, it is not possible to query only a few buckets based on the values of the bucketing column. Queries on the table will simultaneously scan all buckets that hit the partition. This setting is suitable for aggregate query analysis of the entire table data, but not suitable for high-concurrency point queries. -- If the data distribution of the OLAP table is Random Distribution, then during data import, single-tablet import mode can be set (set `load_to_single_tablet` to true). Then, during large-volume data import, a task will only write to one tablet when writing data to the corresponding partition. This can improve the concurrency and throughput of data import, reduce the write amplification caused by data import and compaction, and ensure the stability of the cluster. diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/auto-bucket.md b/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/auto-bucket.md deleted file mode 100644 index ca5ecd76fcfd9..0000000000000 --- a/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/auto-bucket.md +++ /dev/null @@ -1,184 +0,0 @@ ---- -{ - "title": "自动分桶", - "language": "zh-CN" -} ---- - - - - -用户经常设置不合适的 bucket，导致各种问题，这里提供一种方式，来自动设置分桶数。当前只对 OLAP 表生效。 - -:::caution -注意：这个功能在被 CCR 同步时将会失效。如果这个表是被 CCR 复制而来的，即 PROPERTIES 中包含`is_being_synced = true`时，在`show create table`中会显示开启状态，但不会实际生效。当`is_being_synced`被设置为 `false` 时，这些功能将会恢复生效，但`is_being_synced`属性仅供 CCR 外围模块使用，在 CCR 同步的过程中不要手动设置。 -::: - -以往创建分桶时需要手动设定分桶数，而自动分桶推算功能是 Apache Doris 可以动态地推算分桶个数，使得分桶数始终保持在一个合适范围内，让用户不再操心桶数的细枝末节。首先说明一点，为了方便阐述该功能，该部分会将桶拆分为两个时期的桶，即初始分桶以及后续分桶；这里的初始和后续只是本文为了描述清楚该功能而采用的术语，Apache Doris 分桶本身没有初始和后续之分。从上文中创建分桶一节我们知道，BUCKET_DESC 非常简单，但是需要指定分桶个数；而在自动分桶推算功能上，BUCKET_DESC 的语法直接将分桶数改成"Auto"，并新增一个 Properties 配置即可： - -```sql --- 旧版本指定分桶个数的创建语法 -DISTRIBUTED BY HASH(site) BUCKETS 20 - --- 新版本使用自动分桶推算的创建语法 -DISTRIBUTED BY HASH(site) BUCKETS AUTO -properties("estimate_partition_size" = "2G") -``` - -新增的配置参数 estimate_partition_size 表示一个单分区的数据量。该参数是可选的，如果没有给出则 Doris 会将 estimate_partition_size 的默认值取为 10GB。从上文中已经得知，一个分桶在物理层面就是一个 Tablet，为了获得最好的性能，建议 Tablet 的大小在 1GB - 10GB 的范围内。 - -那么自动分桶推算是如何保证 Tablet 大小处于这个范围内的呢？ - -- 若是整体数据量较小，则分桶数不要设置过多 -- 若是整体数据量较大，则应使桶数跟总的磁盘块数相关，充分利用每台 BE 机器和每块磁盘的能力 - -:::tip -estimate_partition_size 属性不支持 alter 操作 -::: - -## 初始分桶推算 - -1. 先根据数据量得出一个桶数 N。首先使用 estimate_partition_size 的值除以 5（按文本格式存入 Doris 中有 5 比 1 的数据压缩比计算），得到的结果为： - - ```Plain - (, 100MB)，则取 N=1 - [100MB, 1GB)，则取 N=2 - [1GB, )，则每 GB 一个分桶 - ``` - -2. 根据 BE 节点数以及每个 BE 节点的磁盘容量，计算出桶数 M。 - - ```Plain - 其中每个 BE 节点算 1，每 50G 的磁盘容量算 1， - M 的计算规则为：M = BE 节点数 * ( 一块磁盘块大小 / 50GB) *磁盘块数 - 举例：有 3 台 BE，每台 BE 都有 4 块 500GB 的磁盘，那么 M = 3 (500GB / 50GB) 4 = 120 - ``` - -3. 得到最终的分桶个数计算逻辑： - - ```Plain - 先计算一个中间值 x = min(M, N, 128)， - 如果 x < N 并且 x < BE 节点个数，则最终分桶为 y 即 BE 节点个数； - 否则最终分桶数为 x - ``` - -4. x = max(x, autobucket_min_buckets), 这里 autobucket_min_buckets 是在 Config 中配置的，默认是 1 - - 上述过程伪代码表现形式为： - - ```Plain - int N = 计算 N 值; - int M = 计算 M 值; - - int y = BE 节点个数; - int x = min(M, N, 128); - - if (x < N && x < y) { - return y; - } - return x; - ``` - -5. 示例：有了上述算法，咱们再引入一些例子来更好地理解这部分逻辑。 - - ```Plain - case1: - 数据量 100 MB，10 台 BE 机器，2TB *3 块盘 - 数据量 N = 1 - BE 磁盘 M = 10* (2TB/50GB) * 3 = 1230 - x = min(M, N, 128) = 1 - 最终：1 - - case2: - 数据量 1GB, 3 台 BE 机器，500GB *2 块盘盘 - 数据量 N = 2 - BE 磁盘 M = 3* (500GB/50GB) * 2 = 60 - x = min(M, N, 128) = 2 - 最终：2 - - case3: - 数据量 100GB，3 台 BE 机器，500GB *2 块盘 - 数据量 N = 20 - BE 磁盘 M = 3* (500GB/50GB) * 2 = 60 - x = min(M, N, 128) = 20 - 最终：20 - - case4: - 数据量 500GB，3 台 BE 机器，1TB *1 块盘 - 数据量 N = 100 - BE 磁盘 M = 3* (1TB /50GB) * 1 = 6060 - x = min(M, N, 128) = 63 - 最终：63 - - case5: - 数据量 500GB，10 台 BE 机器，2TB *3 块盘*3 块盘 - 数据量 N = 100 - BE 磁盘 M = 10* (2TB / 50GB) * 3 = 1230 - x = min(M, N, 128) = 100 - 最终：100 - - case 6: - 数据量 1TB，10 台 BE 机器，2TB *3 块盘 - 数据量 N = 205 - BE 磁盘 M = 10* (2TB / 50GB) * 3 = 1230 - x = min(M, N, 128) = 128 - 最终：128 - - case 7: - 数据量 500GB，1 台 BE 机器，100TB *1 块盘 - 数据量 N = 100 - BE 磁盘 M = 1* (100TB / 50GB) * 1 = 2048 - x = min(M, N, 128) = 100 - 最终：100 - - case 8: - 数据量 1TB, 200 台 BE 机器，4TB *7 块盘 - 数据量 N = 205 - BE 磁盘 M = 200* (4TB / 50GB) * 7 = 114800 - x = min(M, N, 128) = 128 - 最终：200 - ``` - -## 后续分桶推算 - -上述是关于初始分桶的计算逻辑，后续分桶数因为已经有了一定的分区数据，可以根据已有的分区数据量来进行评估。后续分桶数会根据最多前 7 个分区数据量的 EMA（短期指数移动平均线）值，作为 estimate_partition_size 进行评估。此时计算分桶有两种计算方式，假设以天来分区，往前数第一天分区大小为 S7，往前数第二天分区大小为 S6，依次类推到 S1。 - -- 如果 7 天内的分区数据每日严格递增，则此时会取趋势值 - - 有 6 个 delta 值，分别是 - - ```Plain - S7 - S6 = delta1, - S6 - S5 = delta2, - ... - S2 - S1 = delta6 - ``` - - 由此得到 ema(delta) 值：那么，今天的 estimate_partition_size = S7 + ema(delta)。 - -- 非第一种的情况，此时直接取前几天的 EMA 平均值 - - 今天的 estimate_partition_size = EMA(S1, ..., S7)。 - -## 说明 - -根据上述算法，初始分桶个数以及后续分桶个数都能被计算出来。跟之前只能指定固定分桶数不同，由于业务数据的变化，有可能前面分区的分桶数和后面分区的分桶数不一样，这对用户是透明的，用户无需关心每一分区具体的分桶数是多少，而这一自动推算的功能会让分桶数更加合理。 - -开启 autobucket 之后，在`show create table`的时候看到的 schema 也是`BUCKETS AUTO`.如果想要查看确切的 bucket 数，可以通过`show partitions from ${table};`来查看。 diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/data-bucketing.md b/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/data-bucketing.md new file mode 100644 index 0000000000000..ea4b0b3a048c3 --- /dev/null +++ b/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/data-bucketing.md @@ -0,0 +1,214 @@ +--- +{ + "title": "数据分桶", + "language": "zh-CN" +} +--- + + + + + +一个分区可以进一步根据业务逻辑被划分为不同的数据分桶（bucket）。每个分桶会被存储为一个物理的数据分片（tablet）。一个合理的分桶策略能够有效减少查询过程中数据的扫描量，从而提升查询性能并增加查询并发能力。 + +## 分桶方式 + +Doris 支持两种分桶方式：Hash 分桶与 Random 分桶。 + +### Hash 分桶 + +在创建表或新增分区时，用户需选择一列或多列作为分桶列，并明确指定分桶的数量。在同一分区内，系统会根据分桶键和分桶数量进行哈希计算。哈希值相同的数据会被分配到同一个分桶中。例如，在下图中，p250102 分区根据 region 列被划分为 3 个分桶，哈希值相同的行被归入同一个分桶。 + +![hash-bucket](/images/table-desigin/hash-bucket.png) + +在以下场景中，推荐使用 Hash 分桶： + +* 当业务需求频繁根据某个字段进行过滤时，可将该字段作为分桶键进行 Hash 分桶，以提高查询效率。 + +* 当表中数据分布相对均匀时，Hash 分桶同样是一个合适的选择。 + +以下示例展示了如何创建带有 Hash 分桶的表。详细语法请参考 CREATE TABLE 语句。 + +```sql +CREATE TABLE demo.hash_bucket_tbl( + oid BIGINT, + dt DATE, + region VARCHAR(10), + amount INT +) +DUPLICATE KEY(oid) +PARTITION BY RANGE(dt) ( + PARTITION p250101 VALUES LESS THAN("2025-01-01"), + PARTITION p250102 VALUES LESS THAN("2025-01-02") +) +DISTRIBUTED BY HASH(region) BUCKETS 8; +``` + +示例中，通过 `DISTRIBUTED BY HASH(region)` 指定了创建 Hash 分桶，并选择 `region` 列作为分桶键。同时，通过 `BUCKETS 8` 指定了创建 8 个分桶。 + +### Random 分桶 + +在每个分区中，使用 Random 分桶会随机地将数据分散到各个分桶中，不依赖于某个字段的 Hash 值进行数据划分。Random 分桶能够确保数据均匀分散，从而避免由于分桶键选择不当而引发的数据倾斜问题。 + +在导入数据时，单次导入作业的每个批次会被随机写入到一个 tablet 中，以此保证数据的均匀分布。例如，在一次操作中，8 个批次的数据被随机分配到 `p250102` 分区下的 3 个分桶中。 + +![random-bucket](/images/random-bucket.png) + +在使用 Random 分桶时，可以开启单分片导入模式（将 `load_to_single_tablet` 设置为 `true`）。在进行大规模数据导入时，一个批次的数据只会写入到一个数据分片，这有助于提高数据导入的并发度和吞吐量，减少因数据导入和 Compaction 导致的写放大问题，从而保障集群的稳定性。 + +在以下场景中，建议使用 Random 分桶： + +* 在任意维度分析的场景中，业务没有特别针对某一列频繁进行过滤或关联查询时，可以选择 Random 分桶； + +* 当经常查询的列或组合列数据分布极其不均匀时，使用 Random 分桶可以避免数据倾斜。 + +* Random 分桶无法根据分桶键进行剪裁，会扫描命中分区的所有数据，不建议在点查场景下使用； + +* 只有 DUPLICATE 表可以使用 Random 分区，UNIQUE 与 AGGREGATE 表无法使用 Random 分桶； + +以下示例展示了如何创建带有 Random 分桶的表。详细语法请参考 CREATE TABLE 语句： + +```sql +CREATE TABLE demo.random_bucket_tbl( + oid BIGINT, + dt DATE, + region VARCHAR(10), + amount INT +) +DUPLICATE KEY(oid) +PARTITION BY RANGE(dt) ( + PARTITION p250101 VALUES LESS THAN("2025-01-01"), + PARTITION p250102 VALUES LESS THAN("2025-01-02") +) +DISTRIBUTED BY RANDOM BUCKETS 8; +``` + +示例中，通过 `DISTRIBUTED BY RANDOM` 语句指定了使用 Random 分桶，创建 Random 分桶无需选择分桶键，通过 `BUCKETS 8` 语句指定创建 8 个分桶。 + +## 选择分桶键 + +:::tip 提示 + +只有 Hash 分桶需要选择分桶键，Random 分桶不需要选择分桶键。 + +::: + +分桶键可以是一列或者多列。如果是 DUPLICATE 表，任何 Key 列与 Value 列都可以作为分桶键。如果是 AGGREGATE 或 UNIQUE 表，为了保证逐渐的聚合性，分桶列必须是 Key 列。 + +通常情况下，可以根据以下规则选择分桶键： + +* **利用查询过滤条件：**使用查询中的过滤条件进行 Hash 分桶，有助于数据的剪裁，减少数据扫描量； + +* **利用高基数列：**选择高基数（唯一值较多）的列进行 Hash 分桶，有助于数据均匀的分散在每一个分桶中； + +* **高并发点查场景：**建议选择单列或较少列进行分桶。点查可能仅触发一个分桶扫描，不同查询之间触发不同分桶扫描的概率较大，从而减小查询间的 IO 影响。 + +* **大吞吐查询场景：**建议选择多列进行分桶，使数据更均匀分布。若查询条件不能包含所有分桶键的等值条件，将增加查询吞吐，降低单个查询延迟。 + +## 选择分桶数量 + +在 Doris 中，一个 bucket 会被存储为一个物理文件（tablet）。一个表的 Tablet 数量等于 partition\_num（分区数）乘以 bucket\_num（分桶数）。一旦指定 Partition 的数量，便不可更改。 + +在确定 bucket 数量时，需预先考虑机器扩容情况。自 2.0 版本起，Doris 支持根据机器资源和集群信息自动设置分区中的分桶数。 + +### 手动设置分桶数 + +通过 `DISTRIBUTED` 语句可以指定分桶数量： + +```sql +-- Set hash bucket num to 8 +DISTRIBUTED BY HASH(region) BUCKETS 8 + +-- Set random bucket num to 8 +DISTRIBUTED BY RANDOM BUCKETS 8 +``` + +在决定分桶数量时，通常遵循数量与大小两个原则，当发生冲突时，优先考虑大小原则： + +* 大小原则：建议一个 tablet 的大小在 1-10G 范围内。过小的 tablet 可能导致聚合效果不佳，增加元数据管理压力；过大的 tablet 则不利于副本迁移、补齐，且会增加 Schema Change 操作的失败重试代价； + +* 数量原则：在不考虑扩容的情况下，一个表的 tablet 数量建议略多于整个集群的磁盘数量。 + +例如，假设有 10 台 BE 机器，每个 BE 一块磁盘，可以按照以下建议进行数据分桶： + +| 单表大小 | 建议分桶数量 | +| ----- | ----------------------------- | +| 500MB | 4-8 个分桶 | +| 5GB | 6-16 个分桶 | +| 50GB | 32 个分桶 | +| 500GB | 建议分区，每个分区 50GB，每个分区 16-32 个分桶 | +| 5TB | 建议分区，每个分区 50GB，每个分桶 16-32 个分桶 | + +:::tip 提示 + +表的数据量可以通过 `SHOW DATA` 命令查看。结果需要除以副本数，及表的数据量。 + +::: + +### 自动设置分桶数 + +自动推算分桶数功能会根据过去一段时间的分区大小，自动预测未来的分区大小，并据此确定分桶数量。 + +```sql +-- Set hash bucket auto +DISTRIBUTED BY HASH(region) BUCKETS AUTO +properties("estimate_partition_size" = "20G") + +-- Set random bucket auto +DISTRIBUTED BY HASH(region) BUCKETS AUTO +properties("estimate_partition_size" = "20G") +``` + +在创建分桶时，可以通过 `estimate_partition_size` 属性来调整前期估算的分区大小。此参数为可选设置，若未给出，Doris 将默认取值为 10GB。请注意，该参数与后期系统通过历史分区数据推算出的未来分区大小无关。 + +## 维护数据分桶 + +:::tip 提示 + +目前，Doris 仅支持修改新增分区的分桶数量，对于以下操作暂不支持： + +1. 不支持修改分桶类型 + +2. 不支持修改分桶键 + +3. 不支持修改已创建的分桶的分桶数量 + +::: + +在建表时，已通过 `DISTRIBUTED` 语句统一指定了每个分区的数量。为了应对数据增长或减少的情况，在动态增加分区时，可以单独指定新分区的分桶数量。以下示例展示了如何通过 `ALTER TABLE` 命令来修改新增分区的分桶数： + +```sql +-- Modify hash bucket table +ALTER TABLE demo.hash_bucket_tbl +ADD PARTITION p250103 VALUES LESS THAN("2025-01-03") +DISTRIBUTED BY HASH(region) BUCKETS 16; + +-- Modify random bucket table +ALTER TABLE demo.random_bucket_tbl +ADD PARTITION p250103 VALUES LESS THAN("2025-01-03") +DISTRIBUTED BY RANDOM BUCKETS 16; + +-- Modify dynamic partition table +ALTER TABLE demo.dynamic_partition_tbl +SET ("dynamic_partition.buckets"="16"); +``` + +在修改分桶数量后，可以通过 SHOW PARTITION 命令查看修改后的分桶数量。 + diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/manual-bucketing.md b/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/manual-bucketing.md deleted file mode 100644 index 65c59bb707b4a..0000000000000 --- a/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/manual-bucketing.md +++ /dev/null @@ -1,68 +0,0 @@ ---- -{ - "title": "手动分桶", - "language": "zh-CN" -} ---- - - - - -如果使用了分区，则 `DISTRIBUTED ...` 语句描述的是数据在各个分区内的划分规则。 - -如果不使用分区，则描述的是对整个表的数据的划分规则。 - -也可以对每个分区单独指定分桶方式。 - -分桶列可以是多列，Aggregate 和 Unique 模型必须为 Key 列，Duplicate 模型可以是 key 列和 value 列。分桶列可以和 Partition 列相同或不同。 - -分桶列的选择，是在 查询吞吐 和 查询并发 之间的一种权衡： - -- 如果选择多个分桶列，则数据分布更均匀。如果一个查询条件不包含所有分桶列的等值条件，那么该查询会触发所有分桶同时扫描，这样查询的吞吐会增加，单个查询的延迟随之降低。这个方式适合大吞吐低并发的查询场景。 - -- 如果仅选择一个或少数分桶列，则对应的点查询可以仅触发一个分桶扫描。此时，当多个点查询并发时，这些查询有较大的概率分别触发不同的分桶扫描，各个查询之间的 IO 影响较小（尤其当不同桶分布在不同磁盘上时），所以这种方式适合高并发的点查询场景。 - -## Bucket 的数量和数据量的建议 - -- 一个表的 Tablet 总数量等于 (Partition num * Bucket num)。 - -- 一个表的 Tablet 数量，在不考虑扩容的情况下，推荐略多于整个集群的磁盘数量。 - -- 单个 Tablet 的数据量理论上没有上下界，但建议在 1G - 10G 的范围内。如果单个 Tablet 数据量过小，则数据的聚合效果不佳，且元数据管理压力大。如果数据量过大，则不利于副本的迁移、补齐，且会增加 Schema Change 或者 Rollup 操作失败重试的代价（这些操作失败重试的粒度是 Tablet）。 - -- 当 Tablet 的数据量原则和数量原则冲突时，建议优先考虑数据量原则。 - -- 在建表时，每个分区的 Bucket 数量统一指定。但是在动态增加分区时（`ADD PARTITION`），可以单独指定新分区的 Bucket 数量。可以利用这个功能方便的应对数据缩小或膨胀。 - -- 一个 Partition 的 Bucket 数量一旦指定，不可更改。所以在确定 Bucket 数量时，需要预先考虑集群扩容的情况。比如当前只有 3 台 host，每台 host 有 1 块盘。如果 Bucket 的数量只设置为 3 或更小，那么后期即使再增加机器，也不能提高并发度。 - -- 举一些例子：假设在有 10 台 BE，每台 BE 一块磁盘的情况下。如果一个表总大小为 500MB，则可以考虑 4-8 个分片。5GB：8-16 个分片。50GB：32 个分片。500GB：建议分区，每个分区大小在 50GB 左右，每个分区 16-32 个分片。5TB：建议分区，每个分区大小在 50GB 左右，每个分区 16-32 个分片。 - -:::tip -表的数据量可以通过 `SHOW DATA` 命令查看（该命令的统计结果会有延迟），结果除以副本数，即表的数据量。 -::: - -## Random Distribution - -- 如果 OLAP 表没有更新类型的字段，将表的数据分桶模式设置为 RANDOM，则可以避免严重的数据倾斜 (数据在导入表对应的分区的时候，单次导入作业每个 batch 的数据将随机选择一个 tablet 进行写入)。 - -- 当表的分桶模式被设置为 RANDOM 时，因为没有分桶列，无法根据分桶列的值仅对几个分桶查询，对表进行查询的时候将对命中分区的全部分桶同时扫描，该设置适合对表数据整体的聚合查询分析而不适合高并发的点查询。 - -- 如果 OLAP 表的是 Random Distribution 的数据分布，那么在数据导入的时候可以设置单分片导入模式（将 `load_to_single_tablet` 设置为 true），那么在大数据量的导入的时候，一个任务在将数据写入对应的分区时将只写入一个分片，这样将能提高数据导入的并发度和吞吐量，减少数据导入和 Compaction 导致的写放大问题，保障集群的稳定性。 diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/auto-bucket.md b/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/auto-bucket.md deleted file mode 100644 index ca5ecd76fcfd9..0000000000000 --- a/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/auto-bucket.md +++ /dev/null @@ -1,184 +0,0 @@ ---- -{ - "title": "自动分桶", - "language": "zh-CN" -} ---- - - - - -用户经常设置不合适的 bucket，导致各种问题，这里提供一种方式，来自动设置分桶数。当前只对 OLAP 表生效。 - -:::caution -注意：这个功能在被 CCR 同步时将会失效。如果这个表是被 CCR 复制而来的，即 PROPERTIES 中包含`is_being_synced = true`时，在`show create table`中会显示开启状态，但不会实际生效。当`is_being_synced`被设置为 `false` 时，这些功能将会恢复生效，但`is_being_synced`属性仅供 CCR 外围模块使用，在 CCR 同步的过程中不要手动设置。 -::: - -以往创建分桶时需要手动设定分桶数，而自动分桶推算功能是 Apache Doris 可以动态地推算分桶个数，使得分桶数始终保持在一个合适范围内，让用户不再操心桶数的细枝末节。首先说明一点，为了方便阐述该功能，该部分会将桶拆分为两个时期的桶，即初始分桶以及后续分桶；这里的初始和后续只是本文为了描述清楚该功能而采用的术语，Apache Doris 分桶本身没有初始和后续之分。从上文中创建分桶一节我们知道，BUCKET_DESC 非常简单，但是需要指定分桶个数；而在自动分桶推算功能上，BUCKET_DESC 的语法直接将分桶数改成"Auto"，并新增一个 Properties 配置即可： - -```sql --- 旧版本指定分桶个数的创建语法 -DISTRIBUTED BY HASH(site) BUCKETS 20 - --- 新版本使用自动分桶推算的创建语法 -DISTRIBUTED BY HASH(site) BUCKETS AUTO -properties("estimate_partition_size" = "2G") -``` - -新增的配置参数 estimate_partition_size 表示一个单分区的数据量。该参数是可选的，如果没有给出则 Doris 会将 estimate_partition_size 的默认值取为 10GB。从上文中已经得知，一个分桶在物理层面就是一个 Tablet，为了获得最好的性能，建议 Tablet 的大小在 1GB - 10GB 的范围内。 - -那么自动分桶推算是如何保证 Tablet 大小处于这个范围内的呢？ - -- 若是整体数据量较小，则分桶数不要设置过多 -- 若是整体数据量较大，则应使桶数跟总的磁盘块数相关，充分利用每台 BE 机器和每块磁盘的能力 - -:::tip -estimate_partition_size 属性不支持 alter 操作 -::: - -## 初始分桶推算 - -1. 先根据数据量得出一个桶数 N。首先使用 estimate_partition_size 的值除以 5（按文本格式存入 Doris 中有 5 比 1 的数据压缩比计算），得到的结果为： - - ```Plain - (, 100MB)，则取 N=1 - [100MB, 1GB)，则取 N=2 - [1GB, )，则每 GB 一个分桶 - ``` - -2. 根据 BE 节点数以及每个 BE 节点的磁盘容量，计算出桶数 M。 - - ```Plain - 其中每个 BE 节点算 1，每 50G 的磁盘容量算 1， - M 的计算规则为：M = BE 节点数 * ( 一块磁盘块大小 / 50GB) *磁盘块数 - 举例：有 3 台 BE，每台 BE 都有 4 块 500GB 的磁盘，那么 M = 3 (500GB / 50GB) 4 = 120 - ``` - -3. 得到最终的分桶个数计算逻辑： - - ```Plain - 先计算一个中间值 x = min(M, N, 128)， - 如果 x < N 并且 x < BE 节点个数，则最终分桶为 y 即 BE 节点个数； - 否则最终分桶数为 x - ``` - -4. x = max(x, autobucket_min_buckets), 这里 autobucket_min_buckets 是在 Config 中配置的，默认是 1 - - 上述过程伪代码表现形式为： - - ```Plain - int N = 计算 N 值; - int M = 计算 M 值; - - int y = BE 节点个数; - int x = min(M, N, 128); - - if (x < N && x < y) { - return y; - } - return x; - ``` - -5. 示例：有了上述算法，咱们再引入一些例子来更好地理解这部分逻辑。 - - ```Plain - case1: - 数据量 100 MB，10 台 BE 机器，2TB *3 块盘 - 数据量 N = 1 - BE 磁盘 M = 10* (2TB/50GB) * 3 = 1230 - x = min(M, N, 128) = 1 - 最终：1 - - case2: - 数据量 1GB, 3 台 BE 机器，500GB *2 块盘盘 - 数据量 N = 2 - BE 磁盘 M = 3* (500GB/50GB) * 2 = 60 - x = min(M, N, 128) = 2 - 最终：2 - - case3: - 数据量 100GB，3 台 BE 机器，500GB *2 块盘 - 数据量 N = 20 - BE 磁盘 M = 3* (500GB/50GB) * 2 = 60 - x = min(M, N, 128) = 20 - 最终：20 - - case4: - 数据量 500GB，3 台 BE 机器，1TB *1 块盘 - 数据量 N = 100 - BE 磁盘 M = 3* (1TB /50GB) * 1 = 6060 - x = min(M, N, 128) = 63 - 最终：63 - - case5: - 数据量 500GB，10 台 BE 机器，2TB *3 块盘*3 块盘 - 数据量 N = 100 - BE 磁盘 M = 10* (2TB / 50GB) * 3 = 1230 - x = min(M, N, 128) = 100 - 最终：100 - - case 6: - 数据量 1TB，10 台 BE 机器，2TB *3 块盘 - 数据量 N = 205 - BE 磁盘 M = 10* (2TB / 50GB) * 3 = 1230 - x = min(M, N, 128) = 128 - 最终：128 - - case 7: - 数据量 500GB，1 台 BE 机器，100TB *1 块盘 - 数据量 N = 100 - BE 磁盘 M = 1* (100TB / 50GB) * 1 = 2048 - x = min(M, N, 128) = 100 - 最终：100 - - case 8: - 数据量 1TB, 200 台 BE 机器，4TB *7 块盘 - 数据量 N = 205 - BE 磁盘 M = 200* (4TB / 50GB) * 7 = 114800 - x = min(M, N, 128) = 128 - 最终：200 - ``` - -## 后续分桶推算 - -上述是关于初始分桶的计算逻辑，后续分桶数因为已经有了一定的分区数据，可以根据已有的分区数据量来进行评估。后续分桶数会根据最多前 7 个分区数据量的 EMA（短期指数移动平均线）值，作为 estimate_partition_size 进行评估。此时计算分桶有两种计算方式，假设以天来分区，往前数第一天分区大小为 S7，往前数第二天分区大小为 S6，依次类推到 S1。 - -- 如果 7 天内的分区数据每日严格递增，则此时会取趋势值 - - 有 6 个 delta 值，分别是 - - ```Plain - S7 - S6 = delta1, - S6 - S5 = delta2, - ... - S2 - S1 = delta6 - ``` - - 由此得到 ema(delta) 值：那么，今天的 estimate_partition_size = S7 + ema(delta)。 - -- 非第一种的情况，此时直接取前几天的 EMA 平均值 - - 今天的 estimate_partition_size = EMA(S1, ..., S7)。 - -## 说明 - -根据上述算法，初始分桶个数以及后续分桶个数都能被计算出来。跟之前只能指定固定分桶数不同，由于业务数据的变化，有可能前面分区的分桶数和后面分区的分桶数不一样，这对用户是透明的，用户无需关心每一分区具体的分桶数是多少，而这一自动推算的功能会让分桶数更加合理。 - -开启 autobucket 之后，在`show create table`的时候看到的 schema 也是`BUCKETS AUTO`.如果想要查看确切的 bucket 数，可以通过`show partitions from ${table};`来查看。 diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/data-bucketing.md b/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/data-bucketing.md new file mode 100644 index 0000000000000..ea4b0b3a048c3 --- /dev/null +++ b/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/data-bucketing.md @@ -0,0 +1,214 @@ +--- +{ + "title": "数据分桶", + "language": "zh-CN" +} +--- + + + + + +一个分区可以进一步根据业务逻辑被划分为不同的数据分桶（bucket）。每个分桶会被存储为一个物理的数据分片（tablet）。一个合理的分桶策略能够有效减少查询过程中数据的扫描量，从而提升查询性能并增加查询并发能力。 + +## 分桶方式 + +Doris 支持两种分桶方式：Hash 分桶与 Random 分桶。 + +### Hash 分桶 + +在创建表或新增分区时，用户需选择一列或多列作为分桶列，并明确指定分桶的数量。在同一分区内，系统会根据分桶键和分桶数量进行哈希计算。哈希值相同的数据会被分配到同一个分桶中。例如，在下图中，p250102 分区根据 region 列被划分为 3 个分桶，哈希值相同的行被归入同一个分桶。 + +![hash-bucket](/images/table-desigin/hash-bucket.png) + +在以下场景中，推荐使用 Hash 分桶： + +* 当业务需求频繁根据某个字段进行过滤时，可将该字段作为分桶键进行 Hash 分桶，以提高查询效率。 + +* 当表中数据分布相对均匀时，Hash 分桶同样是一个合适的选择。 + +以下示例展示了如何创建带有 Hash 分桶的表。详细语法请参考 CREATE TABLE 语句。 + +```sql +CREATE TABLE demo.hash_bucket_tbl( + oid BIGINT, + dt DATE, + region VARCHAR(10), + amount INT +) +DUPLICATE KEY(oid) +PARTITION BY RANGE(dt) ( + PARTITION p250101 VALUES LESS THAN("2025-01-01"), + PARTITION p250102 VALUES LESS THAN("2025-01-02") +) +DISTRIBUTED BY HASH(region) BUCKETS 8; +``` + +示例中，通过 `DISTRIBUTED BY HASH(region)` 指定了创建 Hash 分桶，并选择 `region` 列作为分桶键。同时，通过 `BUCKETS 8` 指定了创建 8 个分桶。 + +### Random 分桶 + +在每个分区中，使用 Random 分桶会随机地将数据分散到各个分桶中，不依赖于某个字段的 Hash 值进行数据划分。Random 分桶能够确保数据均匀分散，从而避免由于分桶键选择不当而引发的数据倾斜问题。 + +在导入数据时，单次导入作业的每个批次会被随机写入到一个 tablet 中，以此保证数据的均匀分布。例如，在一次操作中，8 个批次的数据被随机分配到 `p250102` 分区下的 3 个分桶中。 + +![random-bucket](/images/random-bucket.png) + +在使用 Random 分桶时，可以开启单分片导入模式（将 `load_to_single_tablet` 设置为 `true`）。在进行大规模数据导入时，一个批次的数据只会写入到一个数据分片，这有助于提高数据导入的并发度和吞吐量，减少因数据导入和 Compaction 导致的写放大问题，从而保障集群的稳定性。 + +在以下场景中，建议使用 Random 分桶： + +* 在任意维度分析的场景中，业务没有特别针对某一列频繁进行过滤或关联查询时，可以选择 Random 分桶； + +* 当经常查询的列或组合列数据分布极其不均匀时，使用 Random 分桶可以避免数据倾斜。 + +* Random 分桶无法根据分桶键进行剪裁，会扫描命中分区的所有数据，不建议在点查场景下使用； + +* 只有 DUPLICATE 表可以使用 Random 分区，UNIQUE 与 AGGREGATE 表无法使用 Random 分桶； + +以下示例展示了如何创建带有 Random 分桶的表。详细语法请参考 CREATE TABLE 语句： + +```sql +CREATE TABLE demo.random_bucket_tbl( + oid BIGINT, + dt DATE, + region VARCHAR(10), + amount INT +) +DUPLICATE KEY(oid) +PARTITION BY RANGE(dt) ( + PARTITION p250101 VALUES LESS THAN("2025-01-01"), + PARTITION p250102 VALUES LESS THAN("2025-01-02") +) +DISTRIBUTED BY RANDOM BUCKETS 8; +``` + +示例中，通过 `DISTRIBUTED BY RANDOM` 语句指定了使用 Random 分桶，创建 Random 分桶无需选择分桶键，通过 `BUCKETS 8` 语句指定创建 8 个分桶。 + +## 选择分桶键 + +:::tip 提示 + +只有 Hash 分桶需要选择分桶键，Random 分桶不需要选择分桶键。 + +::: + +分桶键可以是一列或者多列。如果是 DUPLICATE 表，任何 Key 列与 Value 列都可以作为分桶键。如果是 AGGREGATE 或 UNIQUE 表，为了保证逐渐的聚合性，分桶列必须是 Key 列。 + +通常情况下，可以根据以下规则选择分桶键： + +* **利用查询过滤条件：**使用查询中的过滤条件进行 Hash 分桶，有助于数据的剪裁，减少数据扫描量； + +* **利用高基数列：**选择高基数（唯一值较多）的列进行 Hash 分桶，有助于数据均匀的分散在每一个分桶中； + +* **高并发点查场景：**建议选择单列或较少列进行分桶。点查可能仅触发一个分桶扫描，不同查询之间触发不同分桶扫描的概率较大，从而减小查询间的 IO 影响。 + +* **大吞吐查询场景：**建议选择多列进行分桶，使数据更均匀分布。若查询条件不能包含所有分桶键的等值条件，将增加查询吞吐，降低单个查询延迟。 + +## 选择分桶数量 + +在 Doris 中，一个 bucket 会被存储为一个物理文件（tablet）。一个表的 Tablet 数量等于 partition\_num（分区数）乘以 bucket\_num（分桶数）。一旦指定 Partition 的数量，便不可更改。 + +在确定 bucket 数量时，需预先考虑机器扩容情况。自 2.0 版本起，Doris 支持根据机器资源和集群信息自动设置分区中的分桶数。 + +### 手动设置分桶数 + +通过 `DISTRIBUTED` 语句可以指定分桶数量： + +```sql +-- Set hash bucket num to 8 +DISTRIBUTED BY HASH(region) BUCKETS 8 + +-- Set random bucket num to 8 +DISTRIBUTED BY RANDOM BUCKETS 8 +``` + +在决定分桶数量时，通常遵循数量与大小两个原则，当发生冲突时，优先考虑大小原则： + +* 大小原则：建议一个 tablet 的大小在 1-10G 范围内。过小的 tablet 可能导致聚合效果不佳，增加元数据管理压力；过大的 tablet 则不利于副本迁移、补齐，且会增加 Schema Change 操作的失败重试代价； + +* 数量原则：在不考虑扩容的情况下，一个表的 tablet 数量建议略多于整个集群的磁盘数量。 + +例如，假设有 10 台 BE 机器，每个 BE 一块磁盘，可以按照以下建议进行数据分桶： + +| 单表大小 | 建议分桶数量 | +| ----- | ----------------------------- | +| 500MB | 4-8 个分桶 | +| 5GB | 6-16 个分桶 | +| 50GB | 32 个分桶 | +| 500GB | 建议分区，每个分区 50GB，每个分区 16-32 个分桶 | +| 5TB | 建议分区，每个分区 50GB，每个分桶 16-32 个分桶 | + +:::tip 提示 + +表的数据量可以通过 `SHOW DATA` 命令查看。结果需要除以副本数，及表的数据量。 + +::: + +### 自动设置分桶数 + +自动推算分桶数功能会根据过去一段时间的分区大小，自动预测未来的分区大小，并据此确定分桶数量。 + +```sql +-- Set hash bucket auto +DISTRIBUTED BY HASH(region) BUCKETS AUTO +properties("estimate_partition_size" = "20G") + +-- Set random bucket auto +DISTRIBUTED BY HASH(region) BUCKETS AUTO +properties("estimate_partition_size" = "20G") +``` + +在创建分桶时，可以通过 `estimate_partition_size` 属性来调整前期估算的分区大小。此参数为可选设置，若未给出，Doris 将默认取值为 10GB。请注意，该参数与后期系统通过历史分区数据推算出的未来分区大小无关。 + +## 维护数据分桶 + +:::tip 提示 + +目前，Doris 仅支持修改新增分区的分桶数量，对于以下操作暂不支持： + +1. 不支持修改分桶类型 + +2. 不支持修改分桶键 + +3. 不支持修改已创建的分桶的分桶数量 + +::: + +在建表时，已通过 `DISTRIBUTED` 语句统一指定了每个分区的数量。为了应对数据增长或减少的情况，在动态增加分区时，可以单独指定新分区的分桶数量。以下示例展示了如何通过 `ALTER TABLE` 命令来修改新增分区的分桶数： + +```sql +-- Modify hash bucket table +ALTER TABLE demo.hash_bucket_tbl +ADD PARTITION p250103 VALUES LESS THAN("2025-01-03") +DISTRIBUTED BY HASH(region) BUCKETS 16; + +-- Modify random bucket table +ALTER TABLE demo.random_bucket_tbl +ADD PARTITION p250103 VALUES LESS THAN("2025-01-03") +DISTRIBUTED BY RANDOM BUCKETS 16; + +-- Modify dynamic partition table +ALTER TABLE demo.dynamic_partition_tbl +SET ("dynamic_partition.buckets"="16"); +``` + +在修改分桶数量后，可以通过 SHOW PARTITION 命令查看修改后的分桶数量。 + diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/manual-bucketing.md b/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/manual-bucketing.md deleted file mode 100644 index 65c59bb707b4a..0000000000000 --- a/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/manual-bucketing.md +++ /dev/null @@ -1,68 +0,0 @@ ---- -{ - "title": "手动分桶", - "language": "zh-CN" -} ---- - - - - -如果使用了分区，则 `DISTRIBUTED ...` 语句描述的是数据在各个分区内的划分规则。 - -如果不使用分区，则描述的是对整个表的数据的划分规则。 - -也可以对每个分区单独指定分桶方式。 - -分桶列可以是多列，Aggregate 和 Unique 模型必须为 Key 列，Duplicate 模型可以是 key 列和 value 列。分桶列可以和 Partition 列相同或不同。 - -分桶列的选择，是在 查询吞吐 和 查询并发 之间的一种权衡： - -- 如果选择多个分桶列，则数据分布更均匀。如果一个查询条件不包含所有分桶列的等值条件，那么该查询会触发所有分桶同时扫描，这样查询的吞吐会增加，单个查询的延迟随之降低。这个方式适合大吞吐低并发的查询场景。 - -- 如果仅选择一个或少数分桶列，则对应的点查询可以仅触发一个分桶扫描。此时，当多个点查询并发时，这些查询有较大的概率分别触发不同的分桶扫描，各个查询之间的 IO 影响较小（尤其当不同桶分布在不同磁盘上时），所以这种方式适合高并发的点查询场景。 - -## Bucket 的数量和数据量的建议 - -- 一个表的 Tablet 总数量等于 (Partition num * Bucket num)。 - -- 一个表的 Tablet 数量，在不考虑扩容的情况下，推荐略多于整个集群的磁盘数量。 - -- 单个 Tablet 的数据量理论上没有上下界，但建议在 1G - 10G 的范围内。如果单个 Tablet 数据量过小，则数据的聚合效果不佳，且元数据管理压力大。如果数据量过大，则不利于副本的迁移、补齐，且会增加 Schema Change 或者 Rollup 操作失败重试的代价（这些操作失败重试的粒度是 Tablet）。 - -- 当 Tablet 的数据量原则和数量原则冲突时，建议优先考虑数据量原则。 - -- 在建表时，每个分区的 Bucket 数量统一指定。但是在动态增加分区时（`ADD PARTITION`），可以单独指定新分区的 Bucket 数量。可以利用这个功能方便的应对数据缩小或膨胀。 - -- 一个 Partition 的 Bucket 数量一旦指定，不可更改。所以在确定 Bucket 数量时，需要预先考虑集群扩容的情况。比如当前只有 3 台 host，每台 host 有 1 块盘。如果 Bucket 的数量只设置为 3 或更小，那么后期即使再增加机器，也不能提高并发度。 - -- 举一些例子：假设在有 10 台 BE，每台 BE 一块磁盘的情况下。如果一个表总大小为 500MB，则可以考虑 4-8 个分片。5GB：8-16 个分片。50GB：32 个分片。500GB：建议分区，每个分区大小在 50GB 左右，每个分区 16-32 个分片。5TB：建议分区，每个分区大小在 50GB 左右，每个分区 16-32 个分片。 - -:::tip -表的数据量可以通过 `SHOW DATA` 命令查看（该命令的统计结果会有延迟），结果除以副本数，即表的数据量。 -::: - -## Random Distribution - -- 如果 OLAP 表没有更新类型的字段，将表的数据分桶模式设置为 RANDOM，则可以避免严重的数据倾斜 (数据在导入表对应的分区的时候，单次导入作业每个 batch 的数据将随机选择一个 tablet 进行写入)。 - -- 当表的分桶模式被设置为 RANDOM 时，因为没有分桶列，无法根据分桶列的值仅对几个分桶查询，对表进行查询的时候将对命中分区的全部分桶同时扫描，该设置适合对表数据整体的聚合查询分析而不适合高并发的点查询。 - -- 如果 OLAP 表的是 Random Distribution 的数据分布，那么在数据导入的时候可以设置单分片导入模式（将 `load_to_single_tablet` 设置为 true），那么在大数据量的导入的时候，一个任务在将数据写入对应的分区时将只写入一个分片，这样将能提高数据导入的并发度和吞吐量，减少数据导入和 Compaction 导致的写放大问题，保障集群的稳定性。 diff --git a/sidebars.json b/sidebars.json index 333f81e45ce92..2d9b5f612a81a 100644 --- a/sidebars.json +++ b/sidebars.json @@ -104,8 +104,7 @@ "table-design/data-partitioning/manual-partitioning", "table-design/data-partitioning/dynamic-partitioning", "table-design/data-partitioning/auto-partitioning", - "table-design/data-partitioning/manual-bucketing", - "table-design/data-partitioning/auto-bucket", + "table-design/data-partitioning/data-bucketing", "table-design/data-partitioning/common-issues" ] }, diff --git a/versioned_docs/version-2.1/table-design/data-partitioning/data-bucketing.md b/versioned_docs/version-2.1/table-design/data-partitioning/data-bucketing.md new file mode 100644 index 0000000000000..8d249a9e0257e --- /dev/null +++ b/versioned_docs/version-2.1/table-design/data-partitioning/data-bucketing.md @@ -0,0 +1,215 @@ +--- +{ + "title": "数据分桶", + "language": "zh-CN" +} +--- + + + + + +A partition can further be divided into different data buckets based on business logic. Each bucket will be stored as a physical data tablet. A reasonable bucket strategy can effectively reduce the amount of data scanned during queries, thereby improving query performance and increasing query concurrency. + +## Bucket Methods + +Doris supports two bucket methods: Hash Bucketing and Random Bucketing. + +### Hash Bucketing + +When creating a table or adding a partition, users need to select one or more columns as the bucket columns and specify the number of buckets. Within the same partition, the system performs a hash calculation based on the bucket key and the number of buckets. Data with the same hash value will be allocated to the same bucket. For example, in the figure below, partition p250102 is divided into three buckets based on the region column, and rows with the same hash value are placed into the same bucket. + +![hash-bucket](/images/table-desigin/hash-bucket.png) + +It is recommended to use Hash Bucketing in the following scenarios: + +* When the business needs to frequently filter based on a certain field, you can use this field as the bucket key for Hash Bucketing to improve query efficiency. + +* When the data distribution in the table is relatively uniform, Hash Bucketing is also a suitable choice. + +The following example shows how to create a table with Hash Bucketing. For detailed syntax, please refer to the CREATE TABLE statement. + +```sql +CREATE TABLE demo.hash_bucket_tbl( + oid BIGINT, + dt DATE, + region VARCHAR(10), + amount INT +) +DUPLICATE KEY(oid) +PARTITION BY RANGE(dt) ( + PARTITION p250101 VALUES LESS THAN("2025-01-01"), + PARTITION p250102 VALUES LESS THAN("2025-01-02") +) +DISTRIBUTED BY HASH(region) BUCKETS 8; +``` + +In the example, `DISTRIBUTED BY HASH(region)` specifies the creation of Hash Bucketing and selects the `region` column as the bucket key. Meanwhile, `BUCKETS 8` specifies the creation of 8 buckets. + + +### Random Bucketing + +In each partition, Random Bucketing randomly distributes data to various buckets without relying on the hash value of a certain field. Random Bucketing ensures uniform data distribution, thus avoiding data skew caused by improper bucket key selection. + +During data import, each batch of a single import job will be randomly written to a tablet, ensuring uniform data distribution. For example, in one operation, eight batches of data are randomly allocated to three buckets under partition `p250102`. + +![random-bucket](/images/random-bucket.png) + +When using Random Bucketing, you can enable the single-tablet import mode (set `load_to_single_tablet` to `true`). During large-scale data imports, one batch of data will only be written to one data tablet, helping to improve the concurrency and throughput of data imports, reducing write amplification caused by data imports and Compaction, thereby ensuring cluster stability. + +It is recommended to use Random Bucketing in the following scenarios: + +* In scenarios of arbitrary dimension analysis, where the business does not frequently filter or join queries based on a specific column, you can choose Random Bucketing. + +* When the data distribution of frequently queried columns or combinations of columns is extremely uneven, using Random Bucketing can avoid data skew. + +* Random Bucketing cannot be pruned based on bucket keys and will scan all data in the hit partition, so it is not recommended for point query scenarios. + +* Only DUPLICATE tables can use Random partitioning. UNIQUE and AGGREGATE tables cannot use Random Bucketing. + +The following example shows how to create a table with Random Bucketing. For detailed syntax, please refer to the CREATE TABLE statement: + +```sql +CREATE TABLE demo.random_bucket_tbl( + oid BIGINT, + dt DATE, + region VARCHAR(10), + amount INT +) +DUPLICATE KEY(oid) +PARTITION BY RANGE(dt) ( + PARTITION p250101 VALUES LESS THAN("2025-01-01"), + PARTITION p250102 VALUES LESS THAN("2025-01-02") +) +DISTRIBUTED BY RANDOM BUCKETS 8; +``` + +In the example, the statement `DISTRIBUTED BY RANDOM` specifies the use of Random Bucketing. Creating Random Bucketing does not require selecting a bucket key, and the statement `BUCKETS 8` specifies the creation of 8 buckets. + +## Choosing Bucket Keys + +:::tip Note + +Only Hash Bucketing requires selecting bucket keys; Random Bucketing does not require selecting bucket keys. + +::: + +Bucket keys can be one or more columns. If it is a DUPLICATE table, any Key column or Value column can be used as the bucket key. If it is an AGGREGATE or UNIQUE table, to ensure gradual aggregation, the bucket column must be a Key column. + +Generally, you can choose bucket keys based on the following rules: + +* **Using Query Filter Conditions:** Using query filter conditions for Hash Bucketing helps data pruning and reduces data scan volume; + +* **Using High Cardinality Columns:** Selecting high cardinality (many unique values) columns for Hash Bucketing helps to evenly distribute data across each bucket; + +* **High-Concurrency Point Query Scenarios:** It is recommended to select a single column or fewer columns for bucketing. Point queries may only trigger a scan of one bucket, and the probability of different queries triggering scans of different buckets is high, thereby reducing the IO impact between queries. + +* **High-Throughput Query Scenarios:** It is recommended to select multiple columns for bucketing to make data more evenly distributed. If the query conditions cannot include the equality conditions of all bucket keys, it will increase query throughput and reduce the latency of single queries. + +## Choosing the Number of Buckets + +In Doris, a bucket is stored as a physical file (tablet). The number of tablets in a table equals `partition_num` (number of partitions) multiplied by `bucket_num` (number of buckets). Once the number of Partitions is specified, it cannot be changed. + +When determining the number of buckets, you need to consider machine expansion in advance. Starting from version 2.0, Doris supports automatically setting the number of buckets in partitions based on machine resources and cluster information. + +### Manually Setting the Number of Buckets + +You can specify the number of buckets using the `DISTRIBUTED` statement: + +```sql +-- Set hash bucket num to 8 +DISTRIBUTED BY HASH(region) BUCKETS 8 + +-- Set random bucket num to 8 +DISTRIBUTED BY RANDOM BUCKETS 8 +``` + +When determining the number of buckets, two principles are usually followed: quantity and size. If there is a conflict between the two, the size principle is prioritized: + +* **Size Principle:** It is recommended that the size of a tablet should be within the range of 1-10GB. Too small a tablet may result in poor aggregation effect and increase metadata management pressure; too large a tablet is not conducive to replica migration and supplementation and will increase the cost of retrying Schema Change operations. + +* **Quantity Principle:** Without considering expansion, it is recommended that the number of tablets for a table be slightly more than the number of disks in the entire cluster. + +For example, assuming there are 10 BE machines with one disk per BE, you can follow the recommendations below for data bucketing: + +| Table Size | Recommended Number of Buckets | +| ---------- | -------------------------------------- | +| 500MB | 4-8 buckets | +| 5GB | 6-16 buckets | +| 50GB | 32 buckets | +| 500GB | Partition recommended, 50GB per partition, 16-32 buckets per partition | +| 5TB | Partition recommended, 50GB per partition, 16-32 buckets per partition | + +:::tip Note + +The data volume of the table can be viewed using the `SHOW DATA` command. The result needs to be divided by the number of replicas and the data volume of the table. + +::: + +### Automatic Bucket Number Setting + +The automatic bucket number calculation function will automatically predict the future partition size based on the partition size over a period of time, and determine the number of buckets accordingly. + + +```sql +-- Set hash bucket auto +DISTRIBUTED BY HASH(region) BUCKETS AUTO +properties("estimate_partition_size" = "20G") + +-- Set random bucket auto +DISTRIBUTED BY HASH(region) BUCKETS AUTO +properties("estimate_partition_size" = "20G") +``` + +When creating buckets, you can adjust the estimated partition size through the `estimate_partition_size` attribute. This parameter is optional, and if not provided, Doris will default to 10GB. Please note that this parameter is not related to the future partition size calculated by the system based on historical partition data. + +## Maintaining Data Buckets + +:::tip Note + +Currently, Doris only supports modifying the number of buckets in newly added partitions and does not support the following operations: + +1. Modifying the bucketing type +2. Modifying the bucket key +3. Modifying the number of buckets for existing buckets + +::: + +When creating a table, the number of buckets for each partition is uniformly specified through the `DISTRIBUTED` statement. To cope with data growth or reduction, you can specify the number of buckets for the new partition when dynamically adding partitions. The following example shows how to modify the number of buckets in newly added partitions using the `ALTER TABLE` command: + + +```sql +-- Modify hash bucket table +ALTER TABLE demo.hash_bucket_tbl +ADD PARTITION p250103 VALUES LESS THAN("2025-01-03") +DISTRIBUTED BY HASH(region) BUCKETS 16; + +-- Modify random bucket table +ALTER TABLE demo.random_bucket_tbl +ADD PARTITION p250103 VALUES LESS THAN("2025-01-03") +DISTRIBUTED BY RANDOM BUCKETS 16; + +-- Modify dynamic partition table +ALTER TABLE demo.dynamic_partition_tbl +SET ("dynamic_partition.buckets"="16"); +``` + +在修改分桶数量后，可以通过 SHOW PARTITION 命令查看修改后的分桶数量。 + diff --git a/versioned_docs/version-3.0/table-design/data-partitioning/data-bucketing.md b/versioned_docs/version-3.0/table-design/data-partitioning/data-bucketing.md new file mode 100644 index 0000000000000..8d249a9e0257e --- /dev/null +++ b/versioned_docs/version-3.0/table-design/data-partitioning/data-bucketing.md @@ -0,0 +1,215 @@ +--- +{ + "title": "数据分桶", + "language": "zh-CN" +} +--- + + + + + +A partition can further be divided into different data buckets based on business logic. Each bucket will be stored as a physical data tablet. A reasonable bucket strategy can effectively reduce the amount of data scanned during queries, thereby improving query performance and increasing query concurrency. + +## Bucket Methods + +Doris supports two bucket methods: Hash Bucketing and Random Bucketing. + +### Hash Bucketing + +When creating a table or adding a partition, users need to select one or more columns as the bucket columns and specify the number of buckets. Within the same partition, the system performs a hash calculation based on the bucket key and the number of buckets. Data with the same hash value will be allocated to the same bucket. For example, in the figure below, partition p250102 is divided into three buckets based on the region column, and rows with the same hash value are placed into the same bucket. + +![hash-bucket](/images/table-desigin/hash-bucket.png) + +It is recommended to use Hash Bucketing in the following scenarios: + +* When the business needs to frequently filter based on a certain field, you can use this field as the bucket key for Hash Bucketing to improve query efficiency. + +* When the data distribution in the table is relatively uniform, Hash Bucketing is also a suitable choice. + +The following example shows how to create a table with Hash Bucketing. For detailed syntax, please refer to the CREATE TABLE statement. + +```sql +CREATE TABLE demo.hash_bucket_tbl( + oid BIGINT, + dt DATE, + region VARCHAR(10), + amount INT +) +DUPLICATE KEY(oid) +PARTITION BY RANGE(dt) ( + PARTITION p250101 VALUES LESS THAN("2025-01-01"), + PARTITION p250102 VALUES LESS THAN("2025-01-02") +) +DISTRIBUTED BY HASH(region) BUCKETS 8; +``` + +In the example, `DISTRIBUTED BY HASH(region)` specifies the creation of Hash Bucketing and selects the `region` column as the bucket key. Meanwhile, `BUCKETS 8` specifies the creation of 8 buckets. + + +### Random Bucketing + +In each partition, Random Bucketing randomly distributes data to various buckets without relying on the hash value of a certain field. Random Bucketing ensures uniform data distribution, thus avoiding data skew caused by improper bucket key selection. + +During data import, each batch of a single import job will be randomly written to a tablet, ensuring uniform data distribution. For example, in one operation, eight batches of data are randomly allocated to three buckets under partition `p250102`. + +![random-bucket](/images/random-bucket.png) + +When using Random Bucketing, you can enable the single-tablet import mode (set `load_to_single_tablet` to `true`). During large-scale data imports, one batch of data will only be written to one data tablet, helping to improve the concurrency and throughput of data imports, reducing write amplification caused by data imports and Compaction, thereby ensuring cluster stability. + +It is recommended to use Random Bucketing in the following scenarios: + +* In scenarios of arbitrary dimension analysis, where the business does not frequently filter or join queries based on a specific column, you can choose Random Bucketing. + +* When the data distribution of frequently queried columns or combinations of columns is extremely uneven, using Random Bucketing can avoid data skew. + +* Random Bucketing cannot be pruned based on bucket keys and will scan all data in the hit partition, so it is not recommended for point query scenarios. + +* Only DUPLICATE tables can use Random partitioning. UNIQUE and AGGREGATE tables cannot use Random Bucketing. + +The following example shows how to create a table with Random Bucketing. For detailed syntax, please refer to the CREATE TABLE statement: + +```sql +CREATE TABLE demo.random_bucket_tbl( + oid BIGINT, + dt DATE, + region VARCHAR(10), + amount INT +) +DUPLICATE KEY(oid) +PARTITION BY RANGE(dt) ( + PARTITION p250101 VALUES LESS THAN("2025-01-01"), + PARTITION p250102 VALUES LESS THAN("2025-01-02") +) +DISTRIBUTED BY RANDOM BUCKETS 8; +``` + +In the example, the statement `DISTRIBUTED BY RANDOM` specifies the use of Random Bucketing. Creating Random Bucketing does not require selecting a bucket key, and the statement `BUCKETS 8` specifies the creation of 8 buckets. + +## Choosing Bucket Keys + +:::tip Note + +Only Hash Bucketing requires selecting bucket keys; Random Bucketing does not require selecting bucket keys. + +::: + +Bucket keys can be one or more columns. If it is a DUPLICATE table, any Key column or Value column can be used as the bucket key. If it is an AGGREGATE or UNIQUE table, to ensure gradual aggregation, the bucket column must be a Key column. + +Generally, you can choose bucket keys based on the following rules: + +* **Using Query Filter Conditions:** Using query filter conditions for Hash Bucketing helps data pruning and reduces data scan volume; + +* **Using High Cardinality Columns:** Selecting high cardinality (many unique values) columns for Hash Bucketing helps to evenly distribute data across each bucket; + +* **High-Concurrency Point Query Scenarios:** It is recommended to select a single column or fewer columns for bucketing. Point queries may only trigger a scan of one bucket, and the probability of different queries triggering scans of different buckets is high, thereby reducing the IO impact between queries. + +* **High-Throughput Query Scenarios:** It is recommended to select multiple columns for bucketing to make data more evenly distributed. If the query conditions cannot include the equality conditions of all bucket keys, it will increase query throughput and reduce the latency of single queries. + +## Choosing the Number of Buckets + +In Doris, a bucket is stored as a physical file (tablet). The number of tablets in a table equals `partition_num` (number of partitions) multiplied by `bucket_num` (number of buckets). Once the number of Partitions is specified, it cannot be changed. + +When determining the number of buckets, you need to consider machine expansion in advance. Starting from version 2.0, Doris supports automatically setting the number of buckets in partitions based on machine resources and cluster information. + +### Manually Setting the Number of Buckets + +You can specify the number of buckets using the `DISTRIBUTED` statement: + +```sql +-- Set hash bucket num to 8 +DISTRIBUTED BY HASH(region) BUCKETS 8 + +-- Set random bucket num to 8 +DISTRIBUTED BY RANDOM BUCKETS 8 +``` + +When determining the number of buckets, two principles are usually followed: quantity and size. If there is a conflict between the two, the size principle is prioritized: + +* **Size Principle:** It is recommended that the size of a tablet should be within the range of 1-10GB. Too small a tablet may result in poor aggregation effect and increase metadata management pressure; too large a tablet is not conducive to replica migration and supplementation and will increase the cost of retrying Schema Change operations. + +* **Quantity Principle:** Without considering expansion, it is recommended that the number of tablets for a table be slightly more than the number of disks in the entire cluster. + +For example, assuming there are 10 BE machines with one disk per BE, you can follow the recommendations below for data bucketing: + +| Table Size | Recommended Number of Buckets | +| ---------- | -------------------------------------- | +| 500MB | 4-8 buckets | +| 5GB | 6-16 buckets | +| 50GB | 32 buckets | +| 500GB | Partition recommended, 50GB per partition, 16-32 buckets per partition | +| 5TB | Partition recommended, 50GB per partition, 16-32 buckets per partition | + +:::tip Note + +The data volume of the table can be viewed using the `SHOW DATA` command. The result needs to be divided by the number of replicas and the data volume of the table. + +::: + +### Automatic Bucket Number Setting + +The automatic bucket number calculation function will automatically predict the future partition size based on the partition size over a period of time, and determine the number of buckets accordingly. + + +```sql +-- Set hash bucket auto +DISTRIBUTED BY HASH(region) BUCKETS AUTO +properties("estimate_partition_size" = "20G") + +-- Set random bucket auto +DISTRIBUTED BY HASH(region) BUCKETS AUTO +properties("estimate_partition_size" = "20G") +``` + +When creating buckets, you can adjust the estimated partition size through the `estimate_partition_size` attribute. This parameter is optional, and if not provided, Doris will default to 10GB. Please note that this parameter is not related to the future partition size calculated by the system based on historical partition data. + +## Maintaining Data Buckets + +:::tip Note + +Currently, Doris only supports modifying the number of buckets in newly added partitions and does not support the following operations: + +1. Modifying the bucketing type +2. Modifying the bucket key +3. Modifying the number of buckets for existing buckets + +::: + +When creating a table, the number of buckets for each partition is uniformly specified through the `DISTRIBUTED` statement. To cope with data growth or reduction, you can specify the number of buckets for the new partition when dynamically adding partitions. The following example shows how to modify the number of buckets in newly added partitions using the `ALTER TABLE` command: + + +```sql +-- Modify hash bucket table +ALTER TABLE demo.hash_bucket_tbl +ADD PARTITION p250103 VALUES LESS THAN("2025-01-03") +DISTRIBUTED BY HASH(region) BUCKETS 16; + +-- Modify random bucket table +ALTER TABLE demo.random_bucket_tbl +ADD PARTITION p250103 VALUES LESS THAN("2025-01-03") +DISTRIBUTED BY RANDOM BUCKETS 16; + +-- Modify dynamic partition table +ALTER TABLE demo.dynamic_partition_tbl +SET ("dynamic_partition.buckets"="16"); +``` + +在修改分桶数量后，可以通过 SHOW PARTITION 命令查看修改后的分桶数量。 + diff --git a/versioned_sidebars/version-2.1-sidebars.json b/versioned_sidebars/version-2.1-sidebars.json index 5f6841967796f..5af82f7c1c31d 100644 --- a/versioned_sidebars/version-2.1-sidebars.json +++ b/versioned_sidebars/version-2.1-sidebars.json @@ -80,8 +80,7 @@ "table-design/data-partitioning/manual-partitioning", "table-design/data-partitioning/dynamic-partitioning", "table-design/data-partitioning/auto-partitioning", - "table-design/data-partitioning/manual-bucketing", - "table-design/data-partitioning/auto-bucket", + "table-design/data-partitioning/data-bucketing", "table-design/data-partitioning/common-issues" ] }, diff --git a/versioned_sidebars/version-3.0-sidebars.json b/versioned_sidebars/version-3.0-sidebars.json index 511b7e11a0847..ccf99977db5e0 100644 --- a/versioned_sidebars/version-3.0-sidebars.json +++ b/versioned_sidebars/version-3.0-sidebars.json @@ -104,8 +104,7 @@ "table-design/data-partitioning/manual-partitioning", "table-design/data-partitioning/dynamic-partitioning", "table-design/data-partitioning/auto-partitioning", - "table-design/data-partitioning/manual-bucketing", - "table-design/data-partitioning/auto-bucket", + "table-design/data-partitioning/data-bucketing", "table-design/data-partitioning/common-issues" ] }, From dae8e85210ffee9177fb6eedea34427abc155604 Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Fri, 17 Jan 2025 19:34:56 +0800 Subject: [PATCH 06/27] update dynamic partition --- .../table-design/data-partitioning/dynamic-partitioning.md | 2 +- .../table-design/data-partitioning/dynamic-partitioning.md | 2 +- .../table-design/data-partitioning/dynamic-partitioning.md | 2 +- .../table-design/data-partitioning/dynamic-partitioning.md | 2 +- .../table-design/data-partitioning/dynamic-partitioning.md | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/dynamic-partitioning.md b/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/dynamic-partitioning.md index dd757c8850c6b..e41fbd4a6c318 100644 --- a/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/dynamic-partitioning.md +++ b/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/dynamic-partitioning.md @@ -148,7 +148,7 @@ SHOW DYNAMIC PARTITION TABLES; | `dynamic_partition.start` | 否 | 动态分区的起始偏移，为负数。默认值为 -2147483648，即不删除历史分区。根据 `time_unit` 属性的不同，以当天（星期/月）为基准，分区范围在此偏移之前的分区将会被删除。此偏移之后至当前时间的历史分区如不存在，是否创建取决于 `dynamic_partition.create_history_partition`。 | | `dynamic_partition.end` | 是 | 动态分区的结束偏移，为正数。根据 `time_unit` 属性的不同，以当天（星期/月）为基准，提前创建对应范围的分区。 | | `dynamic_partition.prefix` | 是 | 动态创建的分区名前缀。 | -| `dynamic_partition.buckets` | 否 | 动态创建的分区所对应的分桶数量。 | +| `dynamic_partition.buckets` | 否 | 动态创建的分区所对应的分桶数。设置该参数后会覆盖 DISTRIBUTED 中指定的分桶数。量。 | | `dynamic_partition.replication_num` | 否 | 动态创建的分区所对应的副本数量，如果不填写，则默认为该表创建时指定的副本数量。 | | `dynamic_partition.create_history_partition` | 否 | 默认为 false。当置为 true 时，Doris 会自动创建所有分区，具体创建规则见下文。同时，FE 的参数 `max_dynamic_partition_num` 会限制总分区数量，以避免一次性创建过多分区。当期望创建的分区个数大于 `max_dynamic_partition_num` 值时，操作将被禁止。当不指定 `start` 属性时，该参数不生效。 | | `dynamic_partition.history_partition_num` | 否 | 当`create_history_partition` 为 `true` 时，该参数用于指定创建历史分区数量。默认值为 -1，即未设置。该变量与 `dynamic_partition.start` 作用相同，建议同时只设置一个。 | diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/dynamic-partitioning.md b/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/dynamic-partitioning.md index dd757c8850c6b..ada95d5ec57f8 100644 --- a/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/dynamic-partitioning.md +++ b/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/dynamic-partitioning.md @@ -148,7 +148,7 @@ SHOW DYNAMIC PARTITION TABLES; | `dynamic_partition.start` | 否 | 动态分区的起始偏移，为负数。默认值为 -2147483648，即不删除历史分区。根据 `time_unit` 属性的不同，以当天（星期/月）为基准，分区范围在此偏移之前的分区将会被删除。此偏移之后至当前时间的历史分区如不存在，是否创建取决于 `dynamic_partition.create_history_partition`。 | | `dynamic_partition.end` | 是 | 动态分区的结束偏移，为正数。根据 `time_unit` 属性的不同，以当天（星期/月）为基准，提前创建对应范围的分区。 | | `dynamic_partition.prefix` | 是 | 动态创建的分区名前缀。 | -| `dynamic_partition.buckets` | 否 | 动态创建的分区所对应的分桶数量。 | +| `dynamic_partition.buckets` | 否 | 动态创建的分区所对应的分桶数，设置该参数后会覆盖 DISTRIBUTED 中指定的分桶数量。 | | `dynamic_partition.replication_num` | 否 | 动态创建的分区所对应的副本数量，如果不填写，则默认为该表创建时指定的副本数量。 | | `dynamic_partition.create_history_partition` | 否 | 默认为 false。当置为 true 时，Doris 会自动创建所有分区，具体创建规则见下文。同时，FE 的参数 `max_dynamic_partition_num` 会限制总分区数量，以避免一次性创建过多分区。当期望创建的分区个数大于 `max_dynamic_partition_num` 值时，操作将被禁止。当不指定 `start` 属性时，该参数不生效。 | | `dynamic_partition.history_partition_num` | 否 | 当`create_history_partition` 为 `true` 时，该参数用于指定创建历史分区数量。默认值为 -1，即未设置。该变量与 `dynamic_partition.start` 作用相同，建议同时只设置一个。 | diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/dynamic-partitioning.md b/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/dynamic-partitioning.md index dd757c8850c6b..ada95d5ec57f8 100644 --- a/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/dynamic-partitioning.md +++ b/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/dynamic-partitioning.md @@ -148,7 +148,7 @@ SHOW DYNAMIC PARTITION TABLES; | `dynamic_partition.start` | 否 | 动态分区的起始偏移，为负数。默认值为 -2147483648，即不删除历史分区。根据 `time_unit` 属性的不同，以当天（星期/月）为基准，分区范围在此偏移之前的分区将会被删除。此偏移之后至当前时间的历史分区如不存在，是否创建取决于 `dynamic_partition.create_history_partition`。 | | `dynamic_partition.end` | 是 | 动态分区的结束偏移，为正数。根据 `time_unit` 属性的不同，以当天（星期/月）为基准，提前创建对应范围的分区。 | | `dynamic_partition.prefix` | 是 | 动态创建的分区名前缀。 | -| `dynamic_partition.buckets` | 否 | 动态创建的分区所对应的分桶数量。 | +| `dynamic_partition.buckets` | 否 | 动态创建的分区所对应的分桶数，设置该参数后会覆盖 DISTRIBUTED 中指定的分桶数量。 | | `dynamic_partition.replication_num` | 否 | 动态创建的分区所对应的副本数量，如果不填写，则默认为该表创建时指定的副本数量。 | | `dynamic_partition.create_history_partition` | 否 | 默认为 false。当置为 true 时，Doris 会自动创建所有分区，具体创建规则见下文。同时，FE 的参数 `max_dynamic_partition_num` 会限制总分区数量，以避免一次性创建过多分区。当期望创建的分区个数大于 `max_dynamic_partition_num` 值时，操作将被禁止。当不指定 `start` 属性时，该参数不生效。 | | `dynamic_partition.history_partition_num` | 否 | 当`create_history_partition` 为 `true` 时，该参数用于指定创建历史分区数量。默认值为 -1，即未设置。该变量与 `dynamic_partition.start` 作用相同，建议同时只设置一个。 | diff --git a/versioned_docs/version-2.1/table-design/data-partitioning/dynamic-partitioning.md b/versioned_docs/version-2.1/table-design/data-partitioning/dynamic-partitioning.md index 494cc893b261a..307d713e92be0 100644 --- a/versioned_docs/version-2.1/table-design/data-partitioning/dynamic-partitioning.md +++ b/versioned_docs/version-2.1/table-design/data-partitioning/dynamic-partitioning.md @@ -146,7 +146,7 @@ Dynamic partition rule parameters are prefixed with `dynamic_partition` and can | `dynamic_partition.start` | No | The starting offset for dynamic partitions, which is a negative number. The default value is -2147483648, meaning historical partitions are not deleted. Depending on the `time_unit` attribute, partitions before this offset based on the current day (week/month) will be deleted. Whether historical partitions after this offset up to the current time are created depends on `dynamic_partition.create_history_partition`. | | `dynamic_partition.end` | Yes | The ending offset for dynamic partitions, which is a positive number. Depending on the `time_unit` attribute, partitions within the specified range ahead of the current day (week/month) are created in advance. | | `dynamic_partition.prefix` | Yes | The prefix for dynamically created partition names. | -| `dynamic_partition.buckets` | No | The number of buckets corresponding to dynamically created partitions. | +| `dynamic_partition.buckets` | No | The number of buckets corresponding to dynamically created partitions. Setting this parameter will override the number of buckets specified in `DISTRIBUTED`. | | `dynamic_partition.replication_num` | No | The number of replicas corresponding to dynamically created partitions. If not specified, it defaults to the number of replicas specified when the table was created. | | `dynamic_partition.create_history_partition` | No | Defaults to false. When set to true, Doris will automatically create all partitions according to the rules below. Additionally, the FE parameter `max_dynamic_partition_num` will limit the total number of partitions to avoid creating too many partitions at once. If the number of partitions to be created exceeds the `max_dynamic_partition_num` value, the operation will be prohibited. This parameter does not take effect if the `start` attribute is not specified. | | `dynamic_partition.history_partition_num` | No | When `create_history_partition` is set to `true`, this parameter specifies the number of historical partitions to create. The default value is -1, meaning it is not set. This variable functions the same as `dynamic_partition.start`, and it is recommended to set only one of them simultaneously. | diff --git a/versioned_docs/version-3.0/table-design/data-partitioning/dynamic-partitioning.md b/versioned_docs/version-3.0/table-design/data-partitioning/dynamic-partitioning.md index 494cc893b261a..307d713e92be0 100644 --- a/versioned_docs/version-3.0/table-design/data-partitioning/dynamic-partitioning.md +++ b/versioned_docs/version-3.0/table-design/data-partitioning/dynamic-partitioning.md @@ -146,7 +146,7 @@ Dynamic partition rule parameters are prefixed with `dynamic_partition` and can | `dynamic_partition.start` | No | The starting offset for dynamic partitions, which is a negative number. The default value is -2147483648, meaning historical partitions are not deleted. Depending on the `time_unit` attribute, partitions before this offset based on the current day (week/month) will be deleted. Whether historical partitions after this offset up to the current time are created depends on `dynamic_partition.create_history_partition`. | | `dynamic_partition.end` | Yes | The ending offset for dynamic partitions, which is a positive number. Depending on the `time_unit` attribute, partitions within the specified range ahead of the current day (week/month) are created in advance. | | `dynamic_partition.prefix` | Yes | The prefix for dynamically created partition names. | -| `dynamic_partition.buckets` | No | The number of buckets corresponding to dynamically created partitions. | +| `dynamic_partition.buckets` | No | The number of buckets corresponding to dynamically created partitions. Setting this parameter will override the number of buckets specified in `DISTRIBUTED`. | | `dynamic_partition.replication_num` | No | The number of replicas corresponding to dynamically created partitions. If not specified, it defaults to the number of replicas specified when the table was created. | | `dynamic_partition.create_history_partition` | No | Defaults to false. When set to true, Doris will automatically create all partitions according to the rules below. Additionally, the FE parameter `max_dynamic_partition_num` will limit the total number of partitions to avoid creating too many partitions at once. If the number of partitions to be created exceeds the `max_dynamic_partition_num` value, the operation will be prohibited. This parameter does not take effect if the `start` attribute is not specified. | | `dynamic_partition.history_partition_num` | No | When `create_history_partition` is set to `true`, this parameter specifies the number of historical partitions to create. The default value is -1, meaning it is not set. This variable functions the same as `dynamic_partition.start`, and it is recommended to set only one of them simultaneously. | From ac090a4c4a6cba218a739d86373f498d40cffe98 Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Fri, 17 Jan 2025 19:38:10 +0800 Subject: [PATCH 07/27] update dynamic partition --- .../data-partitioning/dynamic-partitioning.md | 230 ------------------ 1 file changed, 230 deletions(-) diff --git a/docs/table-design/data-partitioning/dynamic-partitioning.md b/docs/table-design/data-partitioning/dynamic-partitioning.md index 494cc893b261a..e69de29bb2d1d 100644 --- a/docs/table-design/data-partitioning/dynamic-partitioning.md +++ b/docs/table-design/data-partitioning/dynamic-partitioning.md @@ -1,230 +0,0 @@ ---- -{ - "title": "Dynamic Partitioning", - "language": "zh-CN" -} ---- - - - -Dynamic partitioning will add and remove partitions in a rolling manner according to predefined rules, thereby managing the lifecycle of table partitions (TTL) and reducing data storage pressure. In scenarios such as log management and time-series data management, dynamic partitioning can typically be used to roll-delete expired data. - -The diagram below illustrates lifecycle management using dynamic partitioning, with the following rules specified: - -* The dynamic partition scheduling unit `dynamic_partition.time_unit` is set to DAY, organizing partitions by day; -* The dynamic partition start offset `dynamic_partition.start` is set to -1, retaining the partition from one day ago; -* The dynamic partition end offset `dynamic_partition.end` is set to 2, retaining partitions for the next two days. - -According to the above rules, as time progresses, a total of 4 partitions will always be retained: the partition from the past day, the current day partition, and the partitions for the next two days. - - -![dynamic-partition](/images/getting-started/dynamic-partition.png) - -## Usage Restrictions - -When using dynamic partitioning, the following rules must be followed: - -* Dynamic partitioning will fail when used simultaneously with Cross-Cluster Replication (CCR). -* Dynamic partitioning only supports Range type partitions on DATE/DATETIME columns. -* Dynamic partitioning only supports a single partition key. - -## Creating Dynamic Partitions - -When creating a table, you can create a dynamic partitioned table by specifying the `dynamic_partition` property. - - -```sql -CREATE TABLE test_dynamic_partition( - order_id BIGINT, - create_dt DATE, - username VARCHAR(20) -) -DUPLICATE KEY(order_id) -PARTITION BY RANGE(create_dt) () -DISTRIBUTED BY HASH(order_id) BUCKETS 10 -PROPERTIES( - "dynamic_partition.enable" = "true", - "dynamic_partition.time_unit" = "DAY", - "dynamic_partition.start" = "-1", - "dynamic_partition.end" = "2", - "dynamic_partition.prefix" = "p", - "dynamic_partition.create_history_partition" = "true" -); -``` - -In the example above, a dynamic partitioned table was created with the following specifications. - -For detailed `dynamic_partition` parameters, refer to [Dynamic Partition Parameter Description](#dynamic-partition-property-parameters). - -## Managing Dynamic Partitions - -### Modifying Dynamic Partition Properties - -:::info Tip: - -When using the ALTER TABLE statement to modify dynamic partitioning, the changes will not take effect immediately. The dynamic partitions will be polled and checked at intervals specified by the `dynamic_partition_check_interval_seconds` parameter to complete the necessary partition creation and deletion operations. - -::: - -In the example below, the ALTER TABLE statement is used to modify a non-dynamic partitioned table to a dynamic partitioned table: - -```sql -CREATE TABLE test_dynamic_partition( - order_id BIGINT, - create_dt DATE, - username VARCHAR(20) -) -DUPLICATE KEY(order_id) -DISTRIBUTED BY HASH(order_id) BUCKETS 10; - -ALTER TABLE test_partition SET ( - "dynamic_partition.enable" = "true", - "dynamic_partition.time_unit" = "DAY", - "dynamic_partition.start" = "-1", - "dynamic_partition.end" = "2", - "dynamic_partition.prefix" = "p", - "dynamic_partition.create_history_partition" = "true" -); - -``` - -### 查看动态分区调度情况 - -通过 [SHOW-DYNAMIC-PARTITION](../../sql-manual/sql-statements/Show-Statements/SHOW-DYNAMIC-PARTITION) 可以查看当前数据库下，所有动态分区表的调度情况： - -```sql -SHOW DYNAMIC PARTITION TABLES; -+-----------+--------+----------+-------------+------+--------+---------+-----------+----------------+---------------------+--------+------------------------+----------------------+-------------------------+ -| TableName | Enable | TimeUnit | Start | End | Prefix | Buckets | StartOf | LastUpdateTime | LastSchedulerTime | State | LastCreatePartitionMsg | LastDropPartitionMsg | ReservedHistoryPeriods | -+-----------+--------+----------+-------------+------+--------+---------+-----------+----------------+---------------------+--------+------------------------+----------------------+-------------------------+ -| d3 | true | WEEK | -3 | 3 | p | 1 | MONDAY | N/A | 2020-05-25 14:29:24 | NORMAL | N/A | N/A | [2021-12-01,2021-12-31] | -| d5 | true | DAY | -7 | 3 | p | 32 | N/A | N/A | 2020-05-25 14:29:24 | NORMAL | N/A | N/A | NULL | -| d4 | true | WEEK | -3 | 3 | p | 1 | WEDNESDAY | N/A | 2020-05-25 14:29:24 | NORMAL | N/A | N/A | NULL | -| d6 | true | MONTH | -2147483648 | 2 | p | 8 | 3rd | N/A | 2020-05-25 14:29:24 | NORMAL | N/A | N/A | NULL | -| d2 | true | DAY | -3 | 3 | p | 32 | N/A | N/A | 2020-05-25 14:29:24 | NORMAL | N/A | N/A | NULL | -| d7 | true | MONTH | -2147483648 | 5 | p | 8 | 24th | N/A | 2020-05-25 14:29:24 | NORMAL | N/A | N/A | NULL | -+-----------+--------+----------+-------------+------+--------+---------+-----------+----------------+---------------------+--------+------------------------+----------------------+-------------------------+ -7 rows in set (0.02 sec) -``` - -### Historical Partition Management - -When specifying the number of dynamic partitions using the `start` and `end` attributes, historical partitions are not created all at once to avoid long waiting times. Only partitions after the current time are created. If you need to create all partitions at once, you must enable the `create_history_partition` parameter. - -For example, if the current date is 2024-10-11 and you set `start = -2` and `end = 2`: - -* If `create_history_partition = true` is specified, all partitions are created immediately, resulting in five partitions: [10-09, 10-13]. -* If `create_history_partition = false` is specified, only partitions from 10-11 onwards are created, resulting in three partitions: [10-11, 10-13]. - -## Dynamic Partition Parameter Description - -### Dynamic Partition Property Parameters - -Dynamic partition rule parameters are prefixed with `dynamic_partition` and can be set with the following rule parameters: - -| Parameter | Required | Description | -| ------------------------------------------------ | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `dynamic_partition.enable` | No | Whether to enable the dynamic partition feature. Can be set to TRUE or FALSE. If other required dynamic partition parameters are specified, it defaults to TRUE. | -| `dynamic_partition.time_unit` | Yes | The unit of dynamic partition scheduling. Can be set to `HOUR`, `DAY`, `WEEK`, `MONTH`, or `YEAR`, indicating partition creation or deletion by hour, day, week, month, or year respectively. | -| `dynamic_partition.start` | No | The starting offset for dynamic partitions, which is a negative number. The default value is -2147483648, meaning historical partitions are not deleted. Depending on the `time_unit` attribute, partitions before this offset based on the current day (week/month) will be deleted. Whether historical partitions after this offset up to the current time are created depends on `dynamic_partition.create_history_partition`. | -| `dynamic_partition.end` | Yes | The ending offset for dynamic partitions, which is a positive number. Depending on the `time_unit` attribute, partitions within the specified range ahead of the current day (week/month) are created in advance. | -| `dynamic_partition.prefix` | Yes | The prefix for dynamically created partition names. | -| `dynamic_partition.buckets` | No | The number of buckets corresponding to dynamically created partitions. | -| `dynamic_partition.replication_num` | No | The number of replicas corresponding to dynamically created partitions. If not specified, it defaults to the number of replicas specified when the table was created. | -| `dynamic_partition.create_history_partition` | No | Defaults to false. When set to true, Doris will automatically create all partitions according to the rules below. Additionally, the FE parameter `max_dynamic_partition_num` will limit the total number of partitions to avoid creating too many partitions at once. If the number of partitions to be created exceeds the `max_dynamic_partition_num` value, the operation will be prohibited. This parameter does not take effect if the `start` attribute is not specified. | -| `dynamic_partition.history_partition_num` | No | When `create_history_partition` is set to `true`, this parameter specifies the number of historical partitions to create. The default value is -1, meaning it is not set. This variable functions the same as `dynamic_partition.start`, and it is recommended to set only one of them simultaneously. | -| `dynamic_partition.start_day_of_week` | No | When `time_unit` is set to `WEEK`, this parameter specifies the starting day of the week. Values range from 1 to 7, where 1 represents Monday and 7 represents Sunday. The default is 1, meaning the week starts on Monday. | -| `dynamic_partition.start_day_of_month` | No | When `time_unit` is set to `MONTH`, this parameter specifies the starting date of the month. Values range from 1 to 28, where 1 represents the first day of the month and 28 represents the 28th day. The default is 1, meaning the month starts on the first day. Starting on the 29th, 30th, or 31st is not supported to avoid ambiguities caused by leap years or leap months. | -| `dynamic_partition.reserved_history_periods` | No | The time range of historical partitions that need to be retained. When `dynamic_partition.time_unit` is set to "DAY/WEEK/MONTH/YEAR", it should be set in the format `[yyyy-MM-dd,yyyy-MM-dd],[...,...]`. When `dynamic_partition.time_unit` is set to "HOUR", it should be set in the format `[yyyy-MM-dd HH:mm:ss,yyyy-MM-dd HH:mm:ss],[...,...]`. If not set, it defaults to `"NULL"`. | -| `dynamic_partition.time_zone` | No | The time zone for dynamic partitioning, which defaults to the server's system time zone, such as `Asia/Shanghai`. For more time zone settings, refer to [Time Zone Management](../../admin-manual/cluster-management/time-zone). | - -### FE Configuration Parameters - -Dynamic partition parameter configurations in FE can be modified in the FE configuration file or via the `ADMIN SET FRONTEND CONFIG` command: - -| Parameter | Default Value | Description | -| --------------------------------------- | ------------- | ------------------------------------------------------------------------------------------------------------ | -| `dynamic_partition_enable` | false | Whether to enable Doris's dynamic partition feature. This parameter only affects partition operations of dynamic partition tables and does not affect regular tables. | -| `dynamic_partition_check_interval_seconds` | 600 | The execution frequency of the dynamic partition thread, in seconds. | -| `max_dynamic_partition_num` | 500 | Limits the maximum number of partitions that can be created when creating a dynamic partition table to avoid creating too many partitions at once. | - -## Dynamic Partition Best Practices - -Example 1: Partition by day, retain partitions for the past 7 days and the current day, and pre-create partitions for the next 3 days. - -```sql -CREATE TABLE tbl1 ( - order_id BIGINT, - create_dt DATE, - username VARCHAR(20) -) -PARTITION BY RANGE(create_dt) () -DISTRIBUTED BY HASH(create_dt) -PROPERTIES ( - "dynamic_partition.enable" = "true", - "dynamic_partition.time_unit" = "DAY", - "dynamic_partition.start" = "-7", - "dynamic_partition.end" = "3", - "dynamic_partition.prefix" = "p", - "dynamic_partition.buckets" = "32" -); -``` - -Example 2: Partition by month, do not delete historical partitions, and pre-create partitions for the next 2 months. Additionally, set the starting day to the 3rd of each month. - -```sql -CREATE TABLE tbl1 ( - order_id BIGINT, - create_dt DATE, - username VARCHAR(20) -) -PARTITION BY RANGE(create_dt) () -DISTRIBUTED BY HASH(create_dt) -PROPERTIES ( - "dynamic_partition.enable" = "true", - "dynamic_partition.time_unit" = "MONTH", - "dynamic_partition.end" = "2", - "dynamic_partition.prefix" = "p", - "dynamic_partition.buckets" = "8", - "dynamic_partition.start_day_of_month" = "3" -); -``` - -Example 3: Partition by day, retain partitions for the past 10 days and the next 10 days, and retain historical data during the periods [2020-06-01, 2020-06-20] and [2020-10-31, 2020-11-15]. - -```sql -CREATE TABLE tbl1 ( - order_id BIGINT, - create_dt DATE, - username VARCHAR(20) -) -PARTITION BY RANGE(create_dt) () -DISTRIBUTED BY HASH(create_dt) -PROPERTIES ( - "dynamic_partition.enable" = "true", - "dynamic_partition.time_unit" = "DAY", - "dynamic_partition.start" = "-10", - "dynamic_partition.end" = "10", - "dynamic_partition.prefix" = "p", - "dynamic_partition.buckets" = "8", - "dynamic_partition.reserved_history_periods"="[2020-06-01,2020-06-20],[2020-10-31,2020-11-15]" -); -``` - From cf5d34ad208b9302170e6baaf3cf78715099e11e Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Fri, 17 Jan 2025 20:17:42 +0800 Subject: [PATCH 08/27] Update basic-concepts.mdx --- docs/table-design/data-partitioning/basic-concepts.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/table-design/data-partitioning/basic-concepts.mdx b/docs/table-design/data-partitioning/basic-concepts.mdx index be6aa0891b3d8..e9f46ab3e23fe 100644 --- a/docs/table-design/data-partitioning/basic-concepts.mdx +++ b/docs/table-design/data-partitioning/basic-concepts.mdx @@ -242,7 +242,7 @@ For detailed suggestions on this feature, see [Auto Partition Conjunct with Dyna

-When the user is not sure of a reasonable number of buckets, [Auto Bucket](./auto-bucket) for Doris to complete the estimation, and the user only needs to provide the estimated amount of table data: +When the user is not sure of a reasonable number of buckets, Auto Bucket for Doris to complete the estimation, and the user only needs to provide the estimated amount of table data: ```sql CREATE TABLE IF NOT EXISTS example_range_tbl From c3e6ec5d848b69cbbb28e4a1c5ef2dd49548122f Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Fri, 17 Jan 2025 20:18:17 +0800 Subject: [PATCH 09/27] Update basic-concepts.mdx --- .../table-design/data-partitioning/basic-concepts.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/versioned_docs/version-2.1/table-design/data-partitioning/basic-concepts.mdx b/versioned_docs/version-2.1/table-design/data-partitioning/basic-concepts.mdx index 76121a9408cc2..f101befc4b175 100644 --- a/versioned_docs/version-2.1/table-design/data-partitioning/basic-concepts.mdx +++ b/versioned_docs/version-2.1/table-design/data-partitioning/basic-concepts.mdx @@ -246,7 +246,7 @@ For detailed suggestions on this feature, see [Auto Partition Conjunct with Dyna

-When the user is not sure of a reasonable number of buckets, [Auto Bucket](./auto-bucket) for Doris to complete the estimation, and the user only needs to provide the estimated amount of table data: +When the user is not sure of a reasonable number of buckets, Auto Bucket for Doris to complete the estimation, and the user only needs to provide the estimated amount of table data: ```sql CREATE TABLE IF NOT EXISTS example_range_tbl From 3aa7aef2ee758b6ebf35a02ebd48b97f769e3375 Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Fri, 17 Jan 2025 20:18:37 +0800 Subject: [PATCH 10/27] Update basic-concepts.mdx --- .../table-design/data-partitioning/basic-concepts.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/versioned_docs/version-3.0/table-design/data-partitioning/basic-concepts.mdx b/versioned_docs/version-3.0/table-design/data-partitioning/basic-concepts.mdx index cfd3443b1cf70..2340bf35cb626 100644 --- a/versioned_docs/version-3.0/table-design/data-partitioning/basic-concepts.mdx +++ b/versioned_docs/version-3.0/table-design/data-partitioning/basic-concepts.mdx @@ -242,7 +242,7 @@ For detailed suggestions on this feature, see [Auto Partition Conjunct with Dyna

-When the user is not sure of a reasonable number of buckets, [Auto Bucket](./auto-bucket) for Doris to complete the estimation, and the user only needs to provide the estimated amount of table data: +When the user is not sure of a reasonable number of buckets, Auto Bucket for Doris to complete the estimation, and the user only needs to provide the estimated amount of table data: ```sql CREATE TABLE IF NOT EXISTS example_range_tbl From 395d1b09ac3478065fcb65882f6230d0cec53ee6 Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Fri, 17 Jan 2025 20:21:20 +0800 Subject: [PATCH 11/27] Update basic-concepts.mdx --- .../current/table-design/data-partitioning/basic-concepts.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/basic-concepts.mdx b/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/basic-concepts.mdx index 192f84123a384..ec5f7955158b6 100644 --- a/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/basic-concepts.mdx +++ b/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/basic-concepts.mdx @@ -243,7 +243,7 @@ PROPERTIES

-当用户不确定合理的分桶数时，可以使用[自动分桶](./auto-bucket)由 Doris 完成估计，用户仅需提供估计的表数据量： +当用户不确定合理的分桶数时，可以使用自动分桶由 Doris 完成估计，用户仅需提供估计的表数据量： ```sql CREATE TABLE IF NOT EXISTS example_range_tbl From 39eb27146c68ced23bcc5ee8bddf936c88137d2a Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Fri, 17 Jan 2025 20:22:07 +0800 Subject: [PATCH 12/27] Update basic-concepts.mdx --- .../table-design/data-partitioning/basic-concepts.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/basic-concepts.mdx b/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/basic-concepts.mdx index c5d3c6cf82366..a08418edc36fe 100644 --- a/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/basic-concepts.mdx +++ b/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/basic-concepts.mdx @@ -247,7 +247,7 @@ PROPERTIES

-当用户不确定合理的分桶数时，可以使用[自动分桶](./auto-bucket)由 Doris 完成估计，用户仅需提供估计的表数据量： +当用户不确定合理的分桶数时，可以使用自动分桶由 Doris 完成估计，用户仅需提供估计的表数据量： ```sql CREATE TABLE IF NOT EXISTS example_range_tbl From 3a35adf132c5c4597a6ff61331a902db749ad0f7 Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Fri, 17 Jan 2025 20:23:03 +0800 Subject: [PATCH 13/27] Update basic-concepts.mdx --- .../table-design/data-partitioning/basic-concepts.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/basic-concepts.mdx b/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/basic-concepts.mdx index 66d56d61ecd10..a846037b2c5ca 100644 --- a/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/basic-concepts.mdx +++ b/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/basic-concepts.mdx @@ -243,7 +243,7 @@ PROPERTIES

-当用户不确定合理的分桶数时，可以使用[自动分桶](./auto-bucket)由 Doris 完成估计，用户仅需提供估计的表数据量： +当用户不确定合理的分桶数时，可以使用自动分桶由 Doris 完成估计，用户仅需提供估计的表数据量： ```sql CREATE TABLE IF NOT EXISTS example_range_tbl From 245c4fb1e809fd9ce9dd3e224988b050a07093a8 Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Fri, 17 Jan 2025 20:53:25 +0800 Subject: [PATCH 14/27] Update data-bucketing.md --- docs/table-design/data-partitioning/data-bucketing.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/table-design/data-partitioning/data-bucketing.md b/docs/table-design/data-partitioning/data-bucketing.md index 8d249a9e0257e..234fff007616d 100644 --- a/docs/table-design/data-partitioning/data-bucketing.md +++ b/docs/table-design/data-partitioning/data-bucketing.md @@ -70,7 +70,7 @@ In each partition, Random Bucketing randomly distributes data to various buckets During data import, each batch of a single import job will be randomly written to a tablet, ensuring uniform data distribution. For example, in one operation, eight batches of data are randomly allocated to three buckets under partition `p250102`. -![random-bucket](/images/random-bucket.png) +![random-bucket](/images/table-desigin/random-bucket.png) When using Random Bucketing, you can enable the single-tablet import mode (set `load_to_single_tablet` to `true`). During large-scale data imports, one batch of data will only be written to one data tablet, helping to improve the concurrency and throughput of data imports, reducing write amplification caused by data imports and Compaction, thereby ensuring cluster stability. From 5f1aa00846880ca5eeacddb833859593656f9943 Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Fri, 17 Jan 2025 20:57:57 +0800 Subject: [PATCH 15/27] Update data-bucketing.md --- .../current/table-design/data-partitioning/data-bucketing.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/data-bucketing.md b/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/data-bucketing.md index ea4b0b3a048c3..bc8cec1c36d52 100644 --- a/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/data-bucketing.md +++ b/i18n/zh-CN/docusaurus-plugin-content-docs/current/table-design/data-partitioning/data-bucketing.md @@ -69,7 +69,7 @@ DISTRIBUTED BY HASH(region) BUCKETS 8; 在导入数据时，单次导入作业的每个批次会被随机写入到一个 tablet 中，以此保证数据的均匀分布。例如，在一次操作中，8 个批次的数据被随机分配到 `p250102` 分区下的 3 个分桶中。 -![random-bucket](/images/random-bucket.png) +![random-bucket](/images/table-desigin/random-bucket.png) 在使用 Random 分桶时，可以开启单分片导入模式（将 `load_to_single_tablet` 设置为 `true`）。在进行大规模数据导入时，一个批次的数据只会写入到一个数据分片，这有助于提高数据导入的并发度和吞吐量，减少因数据导入和 Compaction 导致的写放大问题，从而保障集群的稳定性。 From 7e0080ce948b20af8d19436ab87bc0769cb7c492 Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Fri, 17 Jan 2025 20:58:19 +0800 Subject: [PATCH 16/27] Update data-bucketing.md --- .../table-design/data-partitioning/data-bucketing.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/data-bucketing.md b/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/data-bucketing.md index ea4b0b3a048c3..bc8cec1c36d52 100644 --- a/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/data-bucketing.md +++ b/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/table-design/data-partitioning/data-bucketing.md @@ -69,7 +69,7 @@ DISTRIBUTED BY HASH(region) BUCKETS 8; 在导入数据时，单次导入作业的每个批次会被随机写入到一个 tablet 中，以此保证数据的均匀分布。例如，在一次操作中，8 个批次的数据被随机分配到 `p250102` 分区下的 3 个分桶中。 -![random-bucket](/images/random-bucket.png) +![random-bucket](/images/table-desigin/random-bucket.png) 在使用 Random 分桶时，可以开启单分片导入模式（将 `load_to_single_tablet` 设置为 `true`）。在进行大规模数据导入时，一个批次的数据只会写入到一个数据分片，这有助于提高数据导入的并发度和吞吐量，减少因数据导入和 Compaction 导致的写放大问题，从而保障集群的稳定性。 From a419072b9e3f13652d0f2ef4df98b1af7b210fcf Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Fri, 17 Jan 2025 21:02:19 +0800 Subject: [PATCH 17/27] Update data-bucketing.md --- .../table-design/data-partitioning/data-bucketing.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/data-bucketing.md b/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/data-bucketing.md index ea4b0b3a048c3..bc8cec1c36d52 100644 --- a/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/data-bucketing.md +++ b/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/table-design/data-partitioning/data-bucketing.md @@ -69,7 +69,7 @@ DISTRIBUTED BY HASH(region) BUCKETS 8; 在导入数据时，单次导入作业的每个批次会被随机写入到一个 tablet 中，以此保证数据的均匀分布。例如，在一次操作中，8 个批次的数据被随机分配到 `p250102` 分区下的 3 个分桶中。 -![random-bucket](/images/random-bucket.png) +![random-bucket](/images/table-desigin/random-bucket.png) 在使用 Random 分桶时，可以开启单分片导入模式（将 `load_to_single_tablet` 设置为 `true`）。在进行大规模数据导入时，一个批次的数据只会写入到一个数据分片，这有助于提高数据导入的并发度和吞吐量，减少因数据导入和 Compaction 导致的写放大问题，从而保障集群的稳定性。 From c2524828d62ec7bed7f3aa5f7999d0f09b8f23de Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Fri, 17 Jan 2025 21:02:49 +0800 Subject: [PATCH 18/27] Update data-bucketing.md --- .../table-design/data-partitioning/data-bucketing.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/versioned_docs/version-2.1/table-design/data-partitioning/data-bucketing.md b/versioned_docs/version-2.1/table-design/data-partitioning/data-bucketing.md index 8d249a9e0257e..234fff007616d 100644 --- a/versioned_docs/version-2.1/table-design/data-partitioning/data-bucketing.md +++ b/versioned_docs/version-2.1/table-design/data-partitioning/data-bucketing.md @@ -70,7 +70,7 @@ In each partition, Random Bucketing randomly distributes data to various buckets During data import, each batch of a single import job will be randomly written to a tablet, ensuring uniform data distribution. For example, in one operation, eight batches of data are randomly allocated to three buckets under partition `p250102`. -![random-bucket](/images/random-bucket.png) +![random-bucket](/images/table-desigin/random-bucket.png) When using Random Bucketing, you can enable the single-tablet import mode (set `load_to_single_tablet` to `true`). During large-scale data imports, one batch of data will only be written to one data tablet, helping to improve the concurrency and throughput of data imports, reducing write amplification caused by data imports and Compaction, thereby ensuring cluster stability. From 4a6b72e1870b9d574daac900444655878c64d201 Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Fri, 17 Jan 2025 21:03:27 +0800 Subject: [PATCH 19/27] Update data-bucketing.md --- .../table-design/data-partitioning/data-bucketing.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/versioned_docs/version-3.0/table-design/data-partitioning/data-bucketing.md b/versioned_docs/version-3.0/table-design/data-partitioning/data-bucketing.md index 8d249a9e0257e..234fff007616d 100644 --- a/versioned_docs/version-3.0/table-design/data-partitioning/data-bucketing.md +++ b/versioned_docs/version-3.0/table-design/data-partitioning/data-bucketing.md @@ -70,7 +70,7 @@ In each partition, Random Bucketing randomly distributes data to various buckets During data import, each batch of a single import job will be randomly written to a tablet, ensuring uniform data distribution. For example, in one operation, eight batches of data are randomly allocated to three buckets under partition `p250102`. -![random-bucket](/images/random-bucket.png) +![random-bucket](/images/table-desigin/random-bucket.png) When using Random Bucketing, you can enable the single-tablet import mode (set `load_to_single_tablet` to `true`). During large-scale data imports, one batch of data will only be written to one data tablet, helping to improve the concurrency and throughput of data imports, reducing write amplification caused by data imports and Compaction, thereby ensuring cluster stability. From 2c08b5dcf5f89eea696bd72354b1300969364484 Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Mon, 20 Jan 2025 11:04:09 +0800 Subject: [PATCH 20/27] Update data-bucketing.md --- docs/table-design/data-partitioning/data-bucketing.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/table-design/data-partitioning/data-bucketing.md b/docs/table-design/data-partitioning/data-bucketing.md index 234fff007616d..fb057cb5853fc 100644 --- a/docs/table-design/data-partitioning/data-bucketing.md +++ b/docs/table-design/data-partitioning/data-bucketing.md @@ -1,7 +1,7 @@ --- { - "title": "数据分桶", - "language": "zh-CN" + "title": "Data Bucketing", + "language": "en" } --- From 60520d7ef8d48315c3c2557638cae4296581a8a2 Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Mon, 20 Jan 2025 11:04:38 +0800 Subject: [PATCH 21/27] Update data-bucketing.md --- .../table-design/data-partitioning/data-bucketing.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/versioned_docs/version-2.1/table-design/data-partitioning/data-bucketing.md b/versioned_docs/version-2.1/table-design/data-partitioning/data-bucketing.md index 234fff007616d..fb057cb5853fc 100644 --- a/versioned_docs/version-2.1/table-design/data-partitioning/data-bucketing.md +++ b/versioned_docs/version-2.1/table-design/data-partitioning/data-bucketing.md @@ -1,7 +1,7 @@ --- { - "title": "数据分桶", - "language": "zh-CN" + "title": "Data Bucketing", + "language": "en" } --- From be11febeb7c626a1fd8a0435c1c974be3722042c Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Mon, 20 Jan 2025 11:05:12 +0800 Subject: [PATCH 22/27] Update data-bucketing.md --- .../table-design/data-partitioning/data-bucketing.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/versioned_docs/version-3.0/table-design/data-partitioning/data-bucketing.md b/versioned_docs/version-3.0/table-design/data-partitioning/data-bucketing.md index 234fff007616d..fb057cb5853fc 100644 --- a/versioned_docs/version-3.0/table-design/data-partitioning/data-bucketing.md +++ b/versioned_docs/version-3.0/table-design/data-partitioning/data-bucketing.md @@ -1,7 +1,7 @@ --- { - "title": "数据分桶", - "language": "zh-CN" + "title": "Data Bucketing", + "language": "en" } --- From 5b24b18edcb239a094863209414e7c0e72308f6b Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Mon, 20 Jan 2025 11:06:13 +0800 Subject: [PATCH 23/27] Update data-bucketing.md --- docs/table-design/data-partitioning/data-bucketing.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/table-design/data-partitioning/data-bucketing.md b/docs/table-design/data-partitioning/data-bucketing.md index fb057cb5853fc..7a0e622c7b861 100644 --- a/docs/table-design/data-partitioning/data-bucketing.md +++ b/docs/table-design/data-partitioning/data-bucketing.md @@ -211,5 +211,4 @@ ALTER TABLE demo.dynamic_partition_tbl SET ("dynamic_partition.buckets"="16"); ``` -在修改分桶数量后，可以通过 SHOW PARTITION 命令查看修改后的分桶数量。 - +After modifying the number of buckets, you can use the SHOW PARTITION command to check the updated number of buckets. From 5bf6c39c72bc9981aa38ed9e0dd6085f18f65eb6 Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Mon, 20 Jan 2025 11:06:42 +0800 Subject: [PATCH 24/27] Update data-bucketing.md --- .../table-design/data-partitioning/data-bucketing.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/versioned_docs/version-2.1/table-design/data-partitioning/data-bucketing.md b/versioned_docs/version-2.1/table-design/data-partitioning/data-bucketing.md index fb057cb5853fc..7a0e622c7b861 100644 --- a/versioned_docs/version-2.1/table-design/data-partitioning/data-bucketing.md +++ b/versioned_docs/version-2.1/table-design/data-partitioning/data-bucketing.md @@ -211,5 +211,4 @@ ALTER TABLE demo.dynamic_partition_tbl SET ("dynamic_partition.buckets"="16"); ``` -在修改分桶数量后，可以通过 SHOW PARTITION 命令查看修改后的分桶数量。 - +After modifying the number of buckets, you can use the SHOW PARTITION command to check the updated number of buckets. From 86808adbf909019ad074c6079ae87ed9dfd81158 Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Mon, 20 Jan 2025 11:07:04 +0800 Subject: [PATCH 25/27] Update data-bucketing.md --- .../table-design/data-partitioning/data-bucketing.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/versioned_docs/version-3.0/table-design/data-partitioning/data-bucketing.md b/versioned_docs/version-3.0/table-design/data-partitioning/data-bucketing.md index fb057cb5853fc..7a0e622c7b861 100644 --- a/versioned_docs/version-3.0/table-design/data-partitioning/data-bucketing.md +++ b/versioned_docs/version-3.0/table-design/data-partitioning/data-bucketing.md @@ -211,5 +211,4 @@ ALTER TABLE demo.dynamic_partition_tbl SET ("dynamic_partition.buckets"="16"); ``` -在修改分桶数量后，可以通过 SHOW PARTITION 命令查看修改后的分桶数量。 - +After modifying the number of buckets, you can use the SHOW PARTITION command to check the updated number of buckets. From 10fecfdc36d3f45b6f85b58746c5c92869ba4580 Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Tue, 21 Jan 2025 10:54:35 +0800 Subject: [PATCH 26/27] Update dynamic-partitioning.md --- .../table-design/data-partitioning/dynamic-partitioning.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/versioned_docs/version-3.0/table-design/data-partitioning/dynamic-partitioning.md b/versioned_docs/version-3.0/table-design/data-partitioning/dynamic-partitioning.md index 307d713e92be0..407503f1710af 100644 --- a/versioned_docs/version-3.0/table-design/data-partitioning/dynamic-partitioning.md +++ b/versioned_docs/version-3.0/table-design/data-partitioning/dynamic-partitioning.md @@ -1,7 +1,7 @@ --- { "title": "Dynamic Partitioning", - "language": "zh-CN" + "language": "en" } --- From 84accc31203aa10b28e7e22fb35d9ef82d6005fa Mon Sep 17 00:00:00 2001 From: wangtianyi2004 <376612600@qq.com> Date: Tue, 21 Jan 2025 10:54:59 +0800 Subject: [PATCH 27/27] Update dynamic-partitioning.md --- .../data-partitioning/dynamic-partitioning.md | 230 ++++++++++++++++++ 1 file changed, 230 insertions(+) diff --git a/docs/table-design/data-partitioning/dynamic-partitioning.md b/docs/table-design/data-partitioning/dynamic-partitioning.md index e69de29bb2d1d..e211469680ee0 100644 --- a/docs/table-design/data-partitioning/dynamic-partitioning.md +++ b/docs/table-design/data-partitioning/dynamic-partitioning.md @@ -0,0 +1,230 @@ +--- +{ + "title": "Dynamic Partitioning", + "language": "en" +} +--- + + + +Dynamic partitioning will add and remove partitions in a rolling manner according to predefined rules, thereby managing the lifecycle of table partitions (TTL) and reducing data storage pressure. In scenarios such as log management and time-series data management, dynamic partitioning can typically be used to roll-delete expired data. + +The diagram below illustrates lifecycle management using dynamic partitioning, with the following rules specified: + +* The dynamic partition scheduling unit `dynamic_partition.time_unit` is set to DAY, organizing partitions by day; +* The dynamic partition start offset `dynamic_partition.start` is set to -1, retaining the partition from one day ago; +* The dynamic partition end offset `dynamic_partition.end` is set to 2, retaining partitions for the next two days. + +According to the above rules, as time progresses, a total of 4 partitions will always be retained: the partition from the past day, the current day partition, and the partitions for the next two days. + + +![dynamic-partition](/images/getting-started/dynamic-partition.png) + +## Usage Restrictions + +When using dynamic partitioning, the following rules must be followed: + +* Dynamic partitioning will fail when used simultaneously with Cross-Cluster Replication (CCR). +* Dynamic partitioning only supports Range type partitions on DATE/DATETIME columns. +* Dynamic partitioning only supports a single partition key. + +## Creating Dynamic Partitions + +When creating a table, you can create a dynamic partitioned table by specifying the `dynamic_partition` property. + + +```sql +CREATE TABLE test_dynamic_partition( + order_id BIGINT, + create_dt DATE, + username VARCHAR(20) +) +DUPLICATE KEY(order_id) +PARTITION BY RANGE(create_dt) () +DISTRIBUTED BY HASH(order_id) BUCKETS 10 +PROPERTIES( + "dynamic_partition.enable" = "true", + "dynamic_partition.time_unit" = "DAY", + "dynamic_partition.start" = "-1", + "dynamic_partition.end" = "2", + "dynamic_partition.prefix" = "p", + "dynamic_partition.create_history_partition" = "true" +); +``` + +In the example above, a dynamic partitioned table was created with the following specifications. + +For detailed `dynamic_partition` parameters, refer to [Dynamic Partition Parameter Description](#dynamic-partition-property-parameters). + +## Managing Dynamic Partitions + +### Modifying Dynamic Partition Properties + +:::info Tip: + +When using the ALTER TABLE statement to modify dynamic partitioning, the changes will not take effect immediately. The dynamic partitions will be polled and checked at intervals specified by the `dynamic_partition_check_interval_seconds` parameter to complete the necessary partition creation and deletion operations. + +::: + +In the example below, the ALTER TABLE statement is used to modify a non-dynamic partitioned table to a dynamic partitioned table: + +```sql +CREATE TABLE test_dynamic_partition( + order_id BIGINT, + create_dt DATE, + username VARCHAR(20) +) +DUPLICATE KEY(order_id) +DISTRIBUTED BY HASH(order_id) BUCKETS 10; + +ALTER TABLE test_partition SET ( + "dynamic_partition.enable" = "true", + "dynamic_partition.time_unit" = "DAY", + "dynamic_partition.start" = "-1", + "dynamic_partition.end" = "2", + "dynamic_partition.prefix" = "p", + "dynamic_partition.create_history_partition" = "true" +); + +``` + +### 查看动态分区调度情况 + +通过 [SHOW-DYNAMIC-PARTITION](../../sql-manual/sql-statements/Show-Statements/SHOW-DYNAMIC-PARTITION) 可以查看当前数据库下，所有动态分区表的调度情况： + +```sql +SHOW DYNAMIC PARTITION TABLES; ++-----------+--------+----------+-------------+------+--------+---------+-----------+----------------+---------------------+--------+------------------------+----------------------+-------------------------+ +| TableName | Enable | TimeUnit | Start | End | Prefix | Buckets | StartOf | LastUpdateTime | LastSchedulerTime | State | LastCreatePartitionMsg | LastDropPartitionMsg | ReservedHistoryPeriods | ++-----------+--------+----------+-------------+------+--------+---------+-----------+----------------+---------------------+--------+------------------------+----------------------+-------------------------+ +| d3 | true | WEEK | -3 | 3 | p | 1 | MONDAY | N/A | 2020-05-25 14:29:24 | NORMAL | N/A | N/A | [2021-12-01,2021-12-31] | +| d5 | true | DAY | -7 | 3 | p | 32 | N/A | N/A | 2020-05-25 14:29:24 | NORMAL | N/A | N/A | NULL | +| d4 | true | WEEK | -3 | 3 | p | 1 | WEDNESDAY | N/A | 2020-05-25 14:29:24 | NORMAL | N/A | N/A | NULL | +| d6 | true | MONTH | -2147483648 | 2 | p | 8 | 3rd | N/A | 2020-05-25 14:29:24 | NORMAL | N/A | N/A | NULL | +| d2 | true | DAY | -3 | 3 | p | 32 | N/A | N/A | 2020-05-25 14:29:24 | NORMAL | N/A | N/A | NULL | +| d7 | true | MONTH | -2147483648 | 5 | p | 8 | 24th | N/A | 2020-05-25 14:29:24 | NORMAL | N/A | N/A | NULL | ++-----------+--------+----------+-------------+------+--------+---------+-----------+----------------+---------------------+--------+------------------------+----------------------+-------------------------+ +7 rows in set (0.02 sec) +``` + +### Historical Partition Management + +When specifying the number of dynamic partitions using the `start` and `end` attributes, historical partitions are not created all at once to avoid long waiting times. Only partitions after the current time are created. If you need to create all partitions at once, you must enable the `create_history_partition` parameter. + +For example, if the current date is 2024-10-11 and you set `start = -2` and `end = 2`: + +* If `create_history_partition = true` is specified, all partitions are created immediately, resulting in five partitions: [10-09, 10-13]. +* If `create_history_partition = false` is specified, only partitions from 10-11 onwards are created, resulting in three partitions: [10-11, 10-13]. + +## Dynamic Partition Parameter Description + +### Dynamic Partition Property Parameters + +Dynamic partition rule parameters are prefixed with `dynamic_partition` and can be set with the following rule parameters: + +| Parameter | Required | Description | +| ------------------------------------------------ | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `dynamic_partition.enable` | No | Whether to enable the dynamic partition feature. Can be set to TRUE or FALSE. If other required dynamic partition parameters are specified, it defaults to TRUE. | +| `dynamic_partition.time_unit` | Yes | The unit of dynamic partition scheduling. Can be set to `HOUR`, `DAY`, `WEEK`, `MONTH`, or `YEAR`, indicating partition creation or deletion by hour, day, week, month, or year respectively. | +| `dynamic_partition.start` | No | The starting offset for dynamic partitions, which is a negative number. The default value is -2147483648, meaning historical partitions are not deleted. Depending on the `time_unit` attribute, partitions before this offset based on the current day (week/month) will be deleted. Whether historical partitions after this offset up to the current time are created depends on `dynamic_partition.create_history_partition`. | +| `dynamic_partition.end` | Yes | The ending offset for dynamic partitions, which is a positive number. Depending on the `time_unit` attribute, partitions within the specified range ahead of the current day (week/month) are created in advance. | +| `dynamic_partition.prefix` | Yes | The prefix for dynamically created partition names. | +| `dynamic_partition.buckets` | No | The number of buckets corresponding to dynamically created partitions. Setting this parameter will override the number of buckets specified in `DISTRIBUTED`. | +| `dynamic_partition.replication_num` | No | The number of replicas corresponding to dynamically created partitions. If not specified, it defaults to the number of replicas specified when the table was created. | +| `dynamic_partition.create_history_partition` | No | Defaults to false. When set to true, Doris will automatically create all partitions according to the rules below. Additionally, the FE parameter `max_dynamic_partition_num` will limit the total number of partitions to avoid creating too many partitions at once. If the number of partitions to be created exceeds the `max_dynamic_partition_num` value, the operation will be prohibited. This parameter does not take effect if the `start` attribute is not specified. | +| `dynamic_partition.history_partition_num` | No | When `create_history_partition` is set to `true`, this parameter specifies the number of historical partitions to create. The default value is -1, meaning it is not set. This variable functions the same as `dynamic_partition.start`, and it is recommended to set only one of them simultaneously. | +| `dynamic_partition.start_day_of_week` | No | When `time_unit` is set to `WEEK`, this parameter specifies the starting day of the week. Values range from 1 to 7, where 1 represents Monday and 7 represents Sunday. The default is 1, meaning the week starts on Monday. | +| `dynamic_partition.start_day_of_month` | No | When `time_unit` is set to `MONTH`, this parameter specifies the starting date of the month. Values range from 1 to 28, where 1 represents the first day of the month and 28 represents the 28th day. The default is 1, meaning the month starts on the first day. Starting on the 29th, 30th, or 31st is not supported to avoid ambiguities caused by leap years or leap months. | +| `dynamic_partition.reserved_history_periods` | No | The time range of historical partitions that need to be retained. When `dynamic_partition.time_unit` is set to "DAY/WEEK/MONTH/YEAR", it should be set in the format `[yyyy-MM-dd,yyyy-MM-dd],[...,...]`. When `dynamic_partition.time_unit` is set to "HOUR", it should be set in the format `[yyyy-MM-dd HH:mm:ss,yyyy-MM-dd HH:mm:ss],[...,...]`. If not set, it defaults to `"NULL"`. | +| `dynamic_partition.time_zone` | No | The time zone for dynamic partitioning, which defaults to the server's system time zone, such as `Asia/Shanghai`. For more time zone settings, refer to [Time Zone Management](../../admin-manual/cluster-management/time-zone). | + +### FE Configuration Parameters + +Dynamic partition parameter configurations in FE can be modified in the FE configuration file or via the `ADMIN SET FRONTEND CONFIG` command: + +| Parameter | Default Value | Description | +| --------------------------------------- | ------------- | ------------------------------------------------------------------------------------------------------------ | +| `dynamic_partition_enable` | false | Whether to enable Doris's dynamic partition feature. This parameter only affects partition operations of dynamic partition tables and does not affect regular tables. | +| `dynamic_partition_check_interval_seconds` | 600 | The execution frequency of the dynamic partition thread, in seconds. | +| `max_dynamic_partition_num` | 500 | Limits the maximum number of partitions that can be created when creating a dynamic partition table to avoid creating too many partitions at once. | + +## Dynamic Partition Best Practices + +Example 1: Partition by day, retain partitions for the past 7 days and the current day, and pre-create partitions for the next 3 days. + +```sql +CREATE TABLE tbl1 ( + order_id BIGINT, + create_dt DATE, + username VARCHAR(20) +) +PARTITION BY RANGE(create_dt) () +DISTRIBUTED BY HASH(create_dt) +PROPERTIES ( + "dynamic_partition.enable" = "true", + "dynamic_partition.time_unit" = "DAY", + "dynamic_partition.start" = "-7", + "dynamic_partition.end" = "3", + "dynamic_partition.prefix" = "p", + "dynamic_partition.buckets" = "32" +); +``` + +Example 2: Partition by month, do not delete historical partitions, and pre-create partitions for the next 2 months. Additionally, set the starting day to the 3rd of each month. + +```sql +CREATE TABLE tbl1 ( + order_id BIGINT, + create_dt DATE, + username VARCHAR(20) +) +PARTITION BY RANGE(create_dt) () +DISTRIBUTED BY HASH(create_dt) +PROPERTIES ( + "dynamic_partition.enable" = "true", + "dynamic_partition.time_unit" = "MONTH", + "dynamic_partition.end" = "2", + "dynamic_partition.prefix" = "p", + "dynamic_partition.buckets" = "8", + "dynamic_partition.start_day_of_month" = "3" +); +``` + +Example 3: Partition by day, retain partitions for the past 10 days and the next 10 days, and retain historical data during the periods [2020-06-01, 2020-06-20] and [2020-10-31, 2020-11-15]. + +```sql +CREATE TABLE tbl1 ( + order_id BIGINT, + create_dt DATE, + username VARCHAR(20) +) +PARTITION BY RANGE(create_dt) () +DISTRIBUTED BY HASH(create_dt) +PROPERTIES ( + "dynamic_partition.enable" = "true", + "dynamic_partition.time_unit" = "DAY", + "dynamic_partition.start" = "-10", + "dynamic_partition.end" = "10", + "dynamic_partition.prefix" = "p", + "dynamic_partition.buckets" = "8", + "dynamic_partition.reserved_history_periods"="[2020-06-01,2020-06-20],[2020-10-31,2020-11-15]" +); +``` +