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sql: add the Bikeshare example according to English doc #937
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@@ -28,6 +28,45 @@ TiDB 优化器会根据当前数据表的实际情况来选择最优的执行计 | |||
| task | 当前这个 operator 属于什么 task。目前的执行计划分成为两种 task,一种叫 **root** task,在 tidb-server 上执行,一种叫 **cop** task,并行的在 TiKV 上执行。当前的执行计划在 task 级别的拓扑关系是一个 root task 后面可以跟许多 cop task,root task 使用 cop task 的输出结果作为输入。cop task 中执行的也即是 TiDB 下推到 TiKV 上的任务,每个 cop task 分散在 TiKV 集群中,由多个进程共同执行。 | | |||
| operator info | 每个 operator 的详细信息。各个 operator 的 operator info 各有不同,详见 [Operator Info](#operator-info)。 | | |||
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### 用例 | |||
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使用 [bikeshare example database](../bikeshare-example-database.md): |
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The markdown file does not exist.
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@zz-jason The "bikeshare-example-database.md" file was added in this docs PR: pingcap/docs#596
I think you also need to involve the "bikeshare-example-database.md" file here.
@@ -28,6 +28,45 @@ TiDB 优化器会根据当前数据表的实际情况来选择最优的执行计 | |||
| task | 当前这个 operator 属于什么 task。目前的执行计划分成为两种 task,一种叫 **root** task,在 tidb-server 上执行,一种叫 **cop** task,并行的在 TiKV 上执行。当前的执行计划在 task 级别的拓扑关系是一个 root task 后面可以跟许多 cop task,root task 使用 cop task 的输出结果作为输入。cop task 中执行的也即是 TiDB 下推到 TiKV 上的任务,每个 cop task 分散在 TiKV 集群中,由多个进程共同执行。 | | |||
| operator info | 每个 operator 的详细信息。各个 operator 的 operator info 各有不同,详见 [Operator Info](#operator-info)。 | | |||
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One comment not related to this PR. In the first section of this file, we have:
EXPLAIN 可以和 SELECT,DELETE,INSERT,REPLACE,以及 UPDATE 语句一起使用;
maybe we should remove INSERT
and REPLACE
in this PR together.
mysql> explain insert into t1 values(1,1);
ERROR 1105 (HY000): Unsupported type *core.Insert
mysql> explain replace into t1 values(1,1);
ERROR 1105 (HY000): Unsupported type *core.Insert
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Uh, they are bugs, I think we should fix it 😂
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I'll remove them in this PR, let's add them back once the bugs are fixed.
comments addressed, @eurekaka @lamxTyler PTAL |
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LGTM
5 rows in set (0.00 sec) | ||
``` | ||
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在上面的例子中,coprocessor 上读取 `trips` 表上的数据(`TableScan_18`),寻找满足 `start_date BETWEEN '2017-07-01 00:00:00' AND '2017-07-01 23:59:59'` 条件的数据(`Selection_19`),然后计算满足条件的数据行数(`StreamAgg_9`),最后把结果返回给 TiDB。TiDB 汇总各个 coprocessor 返回的结果(`TableReader_21`),并进一步计算所有数据的行数(`StreamAgg_20`),最终把结果返回给客户端。在上面这个查询中,TiDB 根据 `trips` 表的统计信息估算出 `TableScan_18` 的输出结果行数为 19117643.00,满足条件 `start_date BETWEEN '2017-07-01 00:00:00' AND '2017-07-01 23:59:59'` 的有 `8166.73` 条,经过聚合运算后,只有 1 条结果 |
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Please add a Chinese period at the end of this paragraph.
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在上面的例子中,coprocessor 上读取 `trips` 表上的数据(`TableScan_18`),寻找满足 `start_date BETWEEN '2017-07-01 00:00:00' AND '2017-07-01 23:59:59'` 条件的数据(`Selection_19`),然后计算满足条件的数据行数(`StreamAgg_9`),最后把结果返回给 TiDB。TiDB 汇总各个 coprocessor 返回的结果(`TableReader_21`),并进一步计算所有数据的行数(`StreamAgg_20`),最终把结果返回给客户端。在上面这个查询中,TiDB 根据 `trips` 表的统计信息估算出 `TableScan_18` 的输出结果行数为 19117643.00,满足条件 `start_date BETWEEN '2017-07-01 00:00:00' AND '2017-07-01 23:59:59'` 的有 `8166.73` 条,经过聚合运算后,只有 1 条结果 | ||
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上面这个查询中,虽然大部分计算逻辑都下推到了 TiKV 的 coprocessor 上,但是其执行效率还是不够高。我们可以添加适当的索引来消除 `TableScan_18` 对 `trips` 的全表扫,进一步加速查询的执行: |
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- 上面这个查询中 -> 上述查询中
- Delete "我们". Generally, we do not use first-person pronouns in user documents.
4 rows in set (0.01 sec) | ||
``` | ||
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在添加完索引后的新执行计划中,我们使用 `IndexScan_24` 直接读取满足条件 `start_date BETWEEN '2017-07-01 00:00:00' AND '2017-07-01 23:59:59'` 的数据,可以看到,估算的要扫描的数据行数从之前的 `19117643.00` 降到了现在的 `8166.73`。在测试环境中显示,这个查询的执行时间从 50.41 秒降到了 0.00 秒! |
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Delete "我们".
comments addressed, @lilin90 PTAL, thanks |
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LGTM
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