用 EXPLAIN 查看 JOIN 查询的执行计划

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1ekb41w">.css-1ekb41w{width:100%;margin-left:auto;box-sizing:border-box;margin-right:auto;display:block;padding-left:16px;padding-right:16px;}@media (min-width:600px){.css-1ekb41w{padding-left:24px;padding-right:24px;}}@media (min-width:1536px){.css-1ekb41w{max-width:1536px;}}</style><div class="MuiContainer-root MuiContainer-maxWidthXl doc-content css-1ekb41w"><div class="markdown-body MuiBox-root css-0"><h1 id="用-explain-查看-join-查询的执行计划" style="position:relative"><a href="#%E7%94%A8-explain-%E6%9F%A5%E7%9C%8B-join-%E6%9F%A5%E8%AF%A2%E7%9A%84%E6%89%A7%E8%A1%8C%E8%AE%A1%E5%88%92" aria-label="用 explain 查看 join 查询的执行计划 permalink" class="anchor before"><svg aria-hidden="true" focusable="false" height="16" version="1.1" viewbox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"/></svg></a>用 EXPLAIN 查看 JOIN 查询的执行计划</h1><p>SQL 查询中可能会使用 JOIN 进行表连接，可以通过 <code>EXPLAIN</code> 语句来查看 JOIN 查询的执行计划。本文提供多个示例，以帮助用户理解表连接查询是如何执行的。</p><p>在 TiDB 中，SQL 优化器需要确定数据表的连接顺序，且要判断对于某条特定的 SQL 语句，哪一种 Join 算法最为高效。</p><p>本文档使用的示例数据如下：</p><pre><code class="language-sql">CREATE TABLE t1 (id BIGINT NOT NULL PRIMARY KEY auto_increment, pad1 BLOB, pad2 BLOB, pad3 BLOB, int_col INT NOT NULL DEFAULT 0); CREATE TABLE t2 (id BIGINT NOT NULL PRIMARY KEY auto_increment, t1_id BIGINT NOT NULL, pad1 BLOB, pad2 BLOB, pad3 BLOB, INDEX(t1_id)); INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM dual; INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000; INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000; INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000; INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000; INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000; INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000; INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000; INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000; INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000; INSERT INTO t1 SELECT NULL, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024), 0 FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000; INSERT INTO t2 SELECT NULL, a.id, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000; INSERT INTO t2 SELECT NULL, a.id, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000; INSERT INTO t2 SELECT NULL, a.id, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000; INSERT INTO t2 SELECT NULL, a.id, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000; INSERT INTO t2 SELECT NULL, a.id, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000; INSERT INTO t2 SELECT NULL, a.id, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000; INSERT INTO t2 SELECT NULL, a.id, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000; INSERT INTO t2 SELECT NULL, a.id, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000; INSERT INTO t2 SELECT NULL, a.id, RANDOM_BYTES(1024), RANDOM_BYTES(1024), RANDOM_BYTES(1024) FROM t1 a JOIN t1 b JOIN t1 c LIMIT 10000; UPDATE t1 SET int_col = 1 WHERE pad1 = (SELECT pad1 FROM t1 ORDER BY RAND() LIMIT 1); SELECT SLEEP(1); ANALYZE TABLE t1, t2; </code></pre><h2 id="index-join" style="position:relative"><a href="#index-join" aria-label="index join permalink" class="anchor before"><svg aria-hidden="true" focusable="false" height="16" version="1.1" viewbox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"/></svg></a>Index Join</h2><p>如果预计需要连接的行数较少（一般小于 1 万行），推荐使用 Index Join 算法。这个算法与 MySQL 主要使用的 Join 算法类似。在下表的示例中，<code>TableReader_28(Build)</code> 算子首先读取表 <code>t1</code>，然后根据在 <code>t1</code> 中匹配到的每行数据，依次探查表 <code>t2</code> 中的数据：</p><style data-emotion="css 1weqspv">.css-1weqspv{padding-top:0.5rem;padding-bottom:0.5rem;}.css-1weqspv .MuiAlert-message p{margin-bottom:0px;margin-top:0px;}</style><div class="MuiBox-root css-1weqspv"><style data-emotion="css 17zlexa">.css-17zlexa{font-size:0.875rem;line-height:1.25rem;font-family:"Helvetica Neue","sans-serif";font-weight:400;font-style:normal;color:rgb(1, 67, 97);background-color:rgb(229, 246, 253);display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;padding:6px 16px;}.css-17zlexa .MuiAlert-icon{color:#03a9f4;}</style><style data-emotion="css 152gid5">.css-152gid5{background-color:#fff;color:rgba(0, 0, 0, 0.87);-webkit-transition:box-shadow 300ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:box-shadow 300ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;border-radius:4px;box-shadow:none;font-size:0.875rem;line-height:1.25rem;font-family:"Helvetica Neue","sans-serif";font-weight:400;font-style:normal;color:rgb(1, 67, 97);background-color:rgb(229, 246, 253);display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;padding:6px 16px;}.css-152gid5 .MuiAlert-icon{color:#03a9f4;}</style><div class="MuiPaper-root MuiPaper-elevation MuiPaper-rounded MuiPaper-elevation0 MuiAlert-root MuiAlert-standardInfo MuiAlert-standard css-152gid5" role="alert"><style data-emotion="css 1l54tgj">.css-1l54tgj{margin-right:12px;padding:7px 0;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;font-size:22px;opacity:0.9;}</style><div class="MuiAlert-icon css-1l54tgj"><style data-emotion="css 1cw4hi4">.css-1cw4hi4{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;width:1em;height:1em;display:inline-block;fill:currentColor;-webkit-flex-shrink:0;-ms-flex-negative:0;flex-shrink:0;-webkit-transition:fill 200ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:fill 200ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;font-size:inherit;}</style><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeInherit css-1cw4hi4" focusable="false" aria-hidden="true" viewbox="0 0 24 24" data-testid="InfoOutlinedIcon"><path d="M11,9H13V7H11M12,20C7.59,20 4,16.41 4,12C4,7.59 7.59,4 12,4C16.41,4 20,7.59 20, 12C20,16.41 16.41,20 12,20M12,2A10,10 0 0,0 2,12A10,10 0 0,0 12,22A10,10 0 0,0 22,12A10, 10 0 0,0 12,2M11,17H13V11H11V17Z"/></svg></div><style data-emotion="css 1xsto0d">.css-1xsto0d{padding:8px 0;min-width:0;overflow:auto;}</style><div class="MuiAlert-message css-1xsto0d"><style data-emotion="css 1h2y6o4">.css-1h2y6o4{font-weight:500;margin-top:-2px;}</style><style data-emotion="css 17ioyuk">.css-17ioyuk{margin:0;font-size:1rem;line-height:1.5rem;font-family:"Helvetica Neue","sans-serif";font-weight:400;font-style:normal;color:#666666;margin-bottom:0.35em;font-weight:500;margin-top:-2px;}</style><div class="MuiTypography-root MuiTypography-body1 MuiTypography-gutterBottom MuiAlertTitle-root css-17ioyuk">注意</div><p>在执行计划返回结果中，自 v6.4.0 版本起，特定算子（即 <code>IndexJoin</code> 和 <code>Apply</code> 算子的 Probe 端所有子节点）的 <code>estRows</code> 字段意义与 v6.4.0 版本之前的有所不同。细节请参考 <a href="/zh/tidb/stable/explain-overview#解读-explain-的返回结果">TiDB 执行计划概览</a>。</p></div></div></div><pre><code class="language-sql">EXPLAIN SELECT /*+ INL_JOIN(t1, t2) */ * FROM t1 INNER JOIN t2 ON t1.id = t2.t1_id; </code></pre><pre><code class="language-sql">+---------------------------------+----------+-----------+------------------------------+---------------------------------------------------------------------------------------------------------------------------+ | id | estRows | task | access object | operator info | +---------------------------------+----------+-----------+------------------------------+---------------------------------------------------------------------------------------------------------------------------+ | IndexJoin_11 | 90000.00 | root | | inner join, inner:IndexLookUp_10, outer key:test.t1.id, inner key:test.t2.t1_id, equal cond:eq(test.t1.id, test.t2.t1_id) | | ├─TableReader_29(Build) | 71010.00 | root | | data:TableFullScan_28 | | │ └─TableFullScan_28 | 71010.00 | cop[tikv] | table:t1 | keep order:false | | └─IndexLookUp_10(Probe) | 90000.00 | root | | | | ├─IndexRangeScan_8(Build) | 90000.00 | cop[tikv] | table:t2, index:t1_id(t1_id) | range: decided by [eq(test.t2.t1_id, test.t1.id)], keep order:false | | └─TableRowIDScan_9(Probe) | 90000.00 | cop[tikv] | table:t2 | keep order:false | +---------------------------------+----------+-----------+------------------------------+---------------------------------------------------------------------------------------------------------------------------+ </code></pre><p>Index Join 算法对内存消耗较小，但如果需要执行大量探查操作，运行速度可能会慢于其他 Join 算法。以下面这条查询语句为例：</p><pre><code class="language-sql">SELECT * FROM t1 INNER JOIN t2 ON t1.id=t2.t1_id WHERE t1.pad1 = 'value' and t2.pad1='value'; </code></pre><p>在 Inner Join 操作中，TiDB 会先执行 Join Reorder 算法，所以不能确定会先读取 <code>t1</code> 还是 <code>t2</code>。假设 TiDB 先读取了 <code>t1</code> 来构建 Build 端，那么 TiDB 会在探查 <code>t2</code> 前先根据谓词 <code>t1.col = 'value'</code> 筛选数据，但接下来每次探查 <code>t2</code> 时都要应用谓词 <code>t2.col='value'</code>。所以对于这条语句，Index Join 算法可能不如其他 Join 算法高效。</p><p>但如果 Build 端的数据量比 Probe 端小，且 Probe 端的数据已预先建立了索引，那么这种情况下 Index Join 算法效率更高。在下面这段查询语句中，因为 Index Join 比 Hash Join 效率低，所以 SQL 优化器选择了 Hash Join 算法：</p><pre><code class="language-sql">-- 删除已有索引 ALTER TABLE t2 DROP INDEX t1_id; EXPLAIN ANALYZE SELECT /*+ INL_JOIN(t1, t2) */ * FROM t1 INNER JOIN t2 ON t1.id = t2.t1_id WHERE t1.int_col = 1; EXPLAIN ANALYZE SELECT /*+ HASH_JOIN(t1, t2) */ * FROM t1 INNER JOIN t2 ON t1.id = t2.t1_id WHERE t1.int_col = 1; EXPLAIN ANALYZE SELECT * FROM t1 INNER JOIN t2 ON t1.id = t2.t1_id WHERE t1.int_col = 1; </code></pre><pre><code class="language-sql">+-----------------------------+----------+---------+-----------+---------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+---------+------+ | id | estRows | actRows | task | access object | execution info | operator info | memory | disk | +-----------------------------+----------+---------+-----------+---------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+---------+------+ | IndexJoin_14 | 90000.00 | 0 | root | | time:330.2ms, loops:1, inner:{total:72.2ms, concurrency:5, task:12, construct:58.6ms, fetch:13.5ms, build:2.12µs}, probe:26.1ms | inner join, inner:TableReader_10, outer key:test.t2.t1_id, inner key:test.t1.id, equal cond:eq(test.t2.t1_id, test.t1.id) | 88.5 MB | N/A | | ├─TableReader_20(Build) | 90000.00 | 90000 | root | | time:307.2ms, loops:96, cop_task: {num: 24, max: 130.6ms, min: 170.9µs, avg: 33.5ms, p95: 105ms, max_proc_keys: 10687, p95_proc_keys: 9184, tot_proc: 472ms, rpc_num: 24, rpc_time: 802.4ms, copr_cache_hit_ratio: 0.62, distsql_concurrency: 15} | data:TableFullScan_19 | 58.6 MB | N/A | | │ └─TableFullScan_19 | 90000.00 | 90000 | cop[tikv] | table:t2 | tikv_task:{proc max:34ms, min:0s, avg: 15.3ms, p80:24ms, p95:30ms, iters:181, tasks:24}, scan_detail: {total_process_keys: 69744, total_process_keys_size: 217533936, total_keys: 69753, get_snapshot_time: 701.6µs, rocksdb: {delete_skipped_count: 97368, key_skipped_count: 236847, block: {cache_hit_count: 3509}}} | keep order:false | N/A | N/A | | └─TableReader_10(Probe) | 12617.92 | 0 | root | | time:11.9ms, loops:12, cop_task: {num: 42, max: 848.8µs, min: 199µs, avg: 451.8µs, p95: 846.2µs, max_proc_keys: 7, p95_proc_keys: 5, rpc_num: 42, rpc_time: 18.3ms, copr_cache_hit_ratio: 0.00, distsql_concurrency: 15} | data:Selection_9 | N/A | N/A | | └─Selection_9 | 12617.92 | 0 | cop[tikv] | | tikv_task:{proc max:0s, min:0s, avg: 0s, p80:0s, p95:0s, iters:42, tasks:42}, scan_detail: {total_process_keys: 56, total_process_keys_size: 174608, total_keys: 77, get_snapshot_time: 727.7µs, rocksdb: {block: {cache_hit_count: 154}}} | eq(test.t1.int_col, 1) | N/A | N/A | | └─TableRangeScan_8 | 90000.00 | 56 | cop[tikv] | table:t1 | tikv_task:{proc max:0s, min:0s, avg: 0s, p80:0s, p95:0s, iters:42, tasks:42} | range: decided by [test.t2.t1_id], keep order:false | N/A | N/A | +-----------------------------+----------+---------+-----------+---------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+---------+------+ +------------------------------+----------+---------+-----------+---------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------+---------+---------+ | id | estRows | actRows | task | access object | execution info | operator info | memory | disk | +------------------------------+----------+---------+-----------+---------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------+---------+---------+ | HashJoin_20 | 90000.00 | 0 | root | | time:313.6ms, loops:1, build_hash_table:{total:24.6ms, fetch:21.2ms, build:3.32ms}, probe:{concurrency:5, total:1.57s, max:313.5ms, probe:18.9ms, fetch:1.55s} | inner join, equal:[eq(test.t1.id, test.t2.t1_id)] | 32.0 MB | 0 Bytes | | ├─TableReader_23(Build) | 9955.54 | 10000 | root | | time:23.6ms, loops:12, cop_task: {num: 11, max: 504.6µs, min: 203.7µs, avg: 377.4µs, p95: 504.6µs, rpc_num: 11, rpc_time: 3.92ms, copr_cache_hit_ratio: 1.00, distsql_concurrency: 15} | data:Selection_22 | 14.9 MB | N/A | | │ └─Selection_22 | 9955.54 | 10000 | cop[tikv] | | tikv_task:{proc max:104ms, min:3ms, avg: 24.4ms, p80:33ms, p95:104ms, iters:113, tasks:11}, scan_detail: {get_snapshot_time: 241.4µs, rocksdb: {block: {}}} | eq(test.t1.int_col, 1) | N/A | N/A | | │ └─TableFullScan_21 | 71010.00 | 71010 | cop[tikv] | table:t1 | tikv_task:{proc max:101ms, min:3ms, avg: 23.8ms, p80:33ms, p95:101ms, iters:113, tasks:11} | keep order:false | N/A | N/A | | └─TableReader_25(Probe) | 90000.00 | 90000 | root | | time:293.7ms, loops:91, cop_task: {num: 24, max: 105.7ms, min: 210.9µs, avg: 31.4ms, p95: 103.8ms, max_proc_keys: 10687, p95_proc_keys: 9184, tot_proc: 407ms, rpc_num: 24, rpc_time: 752.2ms, copr_cache_hit_ratio: 0.62, distsql_concurrency: 15} | data:TableFullScan_24 | 58.6 MB | N/A | | └─TableFullScan_24 | 90000.00 | 90000 | cop[tikv] | table:t2 | tikv_task:{proc max:31ms, min:0s, avg: 13ms, p80:19ms, p95:26ms, iters:181, tasks:24}, scan_detail: {total_process_keys: 69744, total_process_keys_size: 217533936, total_keys: 69753, get_snapshot_time: 637.2µs, rocksdb: {delete_skipped_count: 97368, key_skipped_count: 236847, block: {cache_hit_count: 3509}}} | keep order:false | N/A | N/A | +------------------------------+----------+---------+-----------+---------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------+---------+---------+ +------------------------------+----------+---------+-----------+---------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------+---------+---------+ | id | estRows | actRows | task | access object | execution info | operator info | memory | disk | +------------------------------+----------+---------+-----------+---------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------+---------+---------+ | HashJoin_21 | 90000.00 | 0 | root | | time:331.7ms, loops:1, build_hash_table:{total:32.7ms, fetch:26ms, build:6.73ms}, probe:{concurrency:5, total:1.66s, max:331.3ms, probe:16ms, fetch:1.64s} | inner join, equal:[eq(test.t1.id, test.t2.t1_id)] | 32.3 MB | 0 Bytes | | ├─TableReader_26(Build) | 9955.54 | 10000 | root | | time:30.4ms, loops:13, cop_task: {num: 11, max: 1.87ms, min: 844.7µs, avg: 1.29ms, p95: 1.87ms, rpc_num: 11, rpc_time: 13.5ms, copr_cache_hit_ratio: 1.00, distsql_concurrency: 15} | data:Selection_25 | 12.2 MB | N/A | | │ └─Selection_25 | 9955.54 | 10000 | cop[tikv] | | tikv_task:{proc max:104ms, min:3ms, avg: 24.4ms, p80:33ms, p95:104ms, iters:113, tasks:11}, scan_detail: {get_snapshot_time: 521µs, rocksdb: {block: {}}} | eq(test.t1.int_col, 1) | N/A | N/A | | │ └─TableFullScan_24 | 71010.00 | 71010 | cop[tikv] | table:t1 | tikv_task:{proc max:101ms, min:3ms, avg: 23.8ms, p80:33ms, p95:101ms, iters:113, tasks:11} | keep order:false | N/A | N/A | | └─TableReader_23(Probe) | 90000.00 | 90000 | root | | time:308.6ms, loops:91, cop_task: {num: 24, max: 123.3ms, min: 518.9µs, avg: 32.4ms, p95: 113.4ms, max_proc_keys: 10687, p95_proc_keys: 9184, tot_proc: 499ms, rpc_num: 24, rpc_time: 776ms, copr_cache_hit_ratio: 0.62, distsql_concurrency: 15} | data:TableFullScan_22 | 58.6 MB | N/A | | └─TableFullScan_22 | 90000.00 | 90000 | cop[tikv] | table:t2 | tikv_task:{proc max:44ms, min:0s, avg: 16.8ms, p80:27ms, p95:40ms, iters:181, tasks:24}, scan_detail: {total_process_keys: 69744, total_process_keys_size: 217533936, total_keys: 69753, get_snapshot_time: 955.4µs, rocksdb: {delete_skipped_count: 97368, key_skipped_count: 236847, block: {cache_hit_count: 3509}}} | keep order:false | N/A | N/A | +------------------------------+----------+---------+-----------+---------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------+---------+---------+ </code></pre><p>在上面所示的 Index Join 操作中，<code>t1.int_col</code> 一列的索引被删除了。如果加上这个索引，操作执行速度可以从 <code>0.3 秒</code> 提高到 <code>0.06 秒</code>，如下表所示：</p><pre><code class="language-sql">-- 重新添加索引 ALTER TABLE t2 ADD INDEX (t1_id); EXPLAIN ANALYZE SELECT /*+ INL_JOIN(t1, t2) */ * FROM t1 INNER JOIN t2 ON t1.id = t2.t1_id WHERE t1.int_col = 1; EXPLAIN ANALYZE SELECT /*+ HASH_JOIN(t1, t2) */ * FROM t1 INNER JOIN t2 ON t1.id = t2.t1_id WHERE t1.int_col = 1; EXPLAIN ANALYZE SELECT * FROM t1 INNER JOIN t2 ON t1.id = t2.t1_id WHERE t1.int_col = 1; </code></pre><pre><code class="language-sql">+----------------------------------+----------+---------+-----------+------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+-----------+------+ | id | estRows | actRows | task | access object | execution info | operator info | memory | disk | +----------------------------------+----------+---------+-----------+------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+-----------+------+ | IndexJoin_12 | 90000.00 | 0 | root | | time:65.6ms, loops:1, inner:{total:129.7ms, concurrency:5, task:7, construct:7.13ms, fetch:122.5ms, build:16.4µs}, probe:2.54ms | inner join, inner:IndexLookUp_11, outer key:test.t1.id, inner key:test.t2.t1_id, equal cond:eq(test.t1.id, test.t2.t1_id) | 28.7 MB | N/A | | ├─TableReader_33(Build) | 9955.54 | 10000 | root | | time:15.4ms, loops:16, cop_task: {num: 11, max: 1.52ms, min: 211.5µs, avg: 416.8µs, p95: 1.52ms, rpc_num: 11, rpc_time: 4.36ms, copr_cache_hit_ratio: 1.00, distsql_concurrency: 15} | data:Selection_32 | 13.9 MB | N/A | | │ └─Selection_32 | 9955.54 | 10000 | cop[tikv] | | tikv_task:{proc max:104ms, min:3ms, avg: 24.4ms, p80:33ms, p95:104ms, iters:113, tasks:11}, scan_detail: {get_snapshot_time: 185µs, rocksdb: {block: {}}} | eq(test.t1.int_col, 1) | N/A | N/A | | │ └─TableFullScan_31 | 71010.00 | 71010 | cop[tikv] | table:t1 | tikv_task:{proc max:101ms, min:3ms, avg: 23.8ms, p80:33ms, p95:101ms, iters:113, tasks:11} | keep order:false | N/A | N/A | | └─IndexLookUp_11(Probe) | 90000.00 | 0 | root | | time:115.6ms, loops:7 | | 555 Bytes | N/A | | ├─IndexRangeScan_9(Build) | 90000.00 | 0 | cop[tikv] | table:t2, index:t1_id(t1_id) | time:114.3ms, loops:7, cop_task: {num: 7, max: 42ms, min: 1.3ms, avg: 16.2ms, p95: 42ms, tot_proc: 71ms, rpc_num: 7, rpc_time: 113.2ms, copr_cache_hit_ratio: 0.29, distsql_concurrency: 15}, tikv_task:{proc max:37ms, min:0s, avg: 11.3ms, p80:20ms, p95:37ms, iters:7, tasks:7}, scan_detail: {total_keys: 9296, get_snapshot_time: 141.9µs, rocksdb: {block: {cache_hit_count: 18592}}} | range: decided by [eq(test.t2.t1_id, test.t1.id)], keep order:false | N/A | N/A | | └─TableRowIDScan_10(Probe) | 90000.00 | 0 | cop[tikv] | table:t2 | | keep order:false | N/A | N/A | +----------------------------------+----------+---------+-----------+------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------+-----------+------+ +------------------------------+----------+---------+-----------+---------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------+---------+---------+ | id | estRows | actRows | task | access object | execution info | operator info | memory | disk | +------------------------------+----------+---------+-----------+---------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------+---------+---------+ | HashJoin_32 | 90000.00 | 0 | root | | time:320.2ms, loops:1, build_hash_table:{total:19.3ms, fetch:16.8ms, build:2.52ms}, probe:{concurrency:5, total:1.6s, max:320.1ms, probe:16.1ms, fetch:1.58s} | inner join, equal:[eq(test.t1.id, test.t2.t1_id)] | 32.0 MB | 0 Bytes | | ├─TableReader_35(Build) | 9955.54 | 10000 | root | | time:18.6ms, loops:12, cop_task: {num: 11, max: 713.8µs, min: 197.3µs, avg: 368.5µs, p95: 713.8µs, rpc_num: 11, rpc_time: 3.83ms, copr_cache_hit_ratio: 1.00, distsql_concurrency: 15} | data:Selection_34 | 14.9 MB | N/A | | │ └─Selection_34 | 9955.54 | 10000 | cop[tikv] | | tikv_task:{proc max:104ms, min:3ms, avg: 24.4ms, p80:33ms, p95:104ms, iters:113, tasks:11}, scan_detail: {get_snapshot_time: 178.9µs, rocksdb: {block: {}}} | eq(test.t1.int_col, 1) | N/A | N/A | | │ └─TableFullScan_33 | 71010.00 | 71010 | cop[tikv] | table:t1 | tikv_task:{proc max:101ms, min:3ms, avg: 23.8ms, p80:33ms, p95:101ms, iters:113, tasks:11} | keep order:false | N/A | N/A | | └─TableReader_37(Probe) | 90000.00 | 90000 | root | | time:304.4ms, loops:91, cop_task: {num: 24, max: 114ms, min: 251.1µs, avg: 33.1ms, p95: 110.4ms, max_proc_keys: 10687, p95_proc_keys: 9184, tot_proc: 492ms, rpc_num: 24, rpc_time: 793ms, copr_cache_hit_ratio: 0.62, distsql_concurrency: 15} | data:TableFullScan_36 | 58.6 MB | N/A | | └─TableFullScan_36 | 90000.00 | 90000 | cop[tikv] | table:t2 | tikv_task:{proc max:38ms, min:3ms, avg: 14.1ms, p80:23ms, p95:35ms, iters:181, tasks:24}, scan_detail: {total_process_keys: 69744, total_process_keys_size: 217533936, total_keys: 139497, get_snapshot_time: 577.2µs, rocksdb: {delete_skipped_count: 44208, key_skipped_count: 253431, block: {cache_hit_count: 3527}}} | keep order:false | N/A | N/A | +------------------------------+----------+---------+-----------+---------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------+---------+---------+ +------------------------------+----------+---------+-----------+---------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------+---------+---------+ | id | estRows | actRows | task | access object | execution info | operator info | memory | disk | +------------------------------+----------+---------+-----------+---------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------+---------+---------+ | HashJoin_33 | 90000.00 | 0 | root | | time:306.3ms, loops:1, build_hash_table:{total:20.5ms, fetch:17.1ms, build:3.45ms}, probe:{concurrency:5, total:1.53s, max:305.9ms, probe:17.1ms, fetch:1.51s} | inner join, equal:[eq(test.t1.id, test.t2.t1_id)] | 32.0 MB | 0 Bytes | | ├─TableReader_42(Build) | 9955.54 | 10000 | root | | time:19.6ms, loops:12, cop_task: {num: 11, max: 1.07ms, min: 246.1µs, avg: 600µs, p95: 1.07ms, rpc_num: 11, rpc_time: 6.17ms, copr_cache_hit_ratio: 1.00, distsql_concurrency: 15} | data:Selection_41 | 19.7 MB | N/A | | │ └─Selection_41 | 9955.54 | 10000 | cop[tikv] | | tikv_task:{proc max:104ms, min:3ms, avg: 24.4ms, p80:33ms, p95:104ms, iters:113, tasks:11}, scan_detail: {get_snapshot_time: 282.9µs, rocksdb: {block: {}}} | eq(test.t1.int_col, 1) | N/A | N/A | | │ └─TableFullScan_40 | 71010.00 | 71010 | cop[tikv] | table:t1 | tikv_task:{proc max:101ms, min:3ms, avg: 23.8ms, p80:33ms, p95:101ms, iters:113, tasks:11} | keep order:false | N/A | N/A | | └─TableReader_44(Probe) | 90000.00 | 90000 | root | | time:289.2ms, loops:91, cop_task: {num: 24, max: 108.2ms, min: 252.8µs, avg: 31.3ms, p95: 106.1ms, max_proc_keys: 10687, p95_proc_keys: 9184, tot_proc: 445ms, rpc_num: 24, rpc_time: 750.4ms, copr_cache_hit_ratio: 0.62, distsql_concurrency: 15} | data:TableFullScan_43 | 58.6 MB | N/A | | └─TableFullScan_43 | 90000.00 | 90000 | cop[tikv] | table:t2 | tikv_task:{proc max:31ms, min:3ms, avg: 13.3ms, p80:24ms, p95:30ms, iters:181, tasks:24}, scan_detail: {total_process_keys: 69744, total_process_keys_size: 217533936, total_keys: 139497, get_snapshot_time: 730.2µs, rocksdb: {delete_skipped_count: 44208, key_skipped_count: 253431, block: {cache_hit_count: 3527}}} | keep order:false | N/A | N/A | +------------------------------+----------+---------+-----------+---------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------+---------+---------+ </code></pre><div class="MuiBox-root css-1weqspv"><div class="MuiPaper-root MuiPaper-elevation MuiPaper-rounded MuiPaper-elevation0 MuiAlert-root MuiAlert-standardInfo MuiAlert-standard css-152gid5" role="alert"><div class="MuiAlert-icon css-1l54tgj"><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeInherit css-1cw4hi4" focusable="false" aria-hidden="true" viewbox="0 0 24 24" data-testid="InfoOutlinedIcon"><path d="M11,9H13V7H11M12,20C7.59,20 4,16.41 4,12C4,7.59 7.59,4 12,4C16.41,4 20,7.59 20, 12C20,16.41 16.41,20 12,20M12,2A10,10 0 0,0 2,12A10,10 0 0,0 12,22A10,10 0 0,0 22,12A10, 10 0 0,0 12,2M11,17H13V11H11V17Z"/></svg></div><div class="MuiAlert-message css-1xsto0d"><div class="MuiTypography-root MuiTypography-body1 MuiTypography-gutterBottom MuiAlertTitle-root css-17ioyuk">注意</div><p>在上方示例中，SQL 优化器之所以选择了性能较差的 Hash Join 算法，而不是 Index Join 算法，原因在于查询优化是一个 <a href="https://zh.wikipedia.org/wiki/NP%E5%AE%8C%E5%85%A8" target="_blank" referrerpolicy="no-referrer-when-downgrade">NP 完全问题</a>，可能会选择不太理想的计划。如果需要频繁调用这个查询，建议通过<a href="/zh/tidb/stable/sql-plan-management">执行计划管理</a>的方式将 Hint 与 SQL 语句绑定，这样要比在发送给 TiDB 的 SQL 语句中插入 Hint 更容易管理。</p></div></div></div><h3 id="index-join-相关算法" style="position:relative"><a href="#index-join-%E7%9B%B8%E5%85%B3%E7%AE%97%E6%B3%95" aria-label="index join 相关算法 permalink" class="anchor before"><svg aria-hidden="true" focusable="false" height="16" version="1.1" viewbox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"/></svg></a>Index Join 相关算法</h3><p>如果使用 Hint <a href="/zh/tidb/stable/optimizer-hints#inl_joint1_name--tl_name-"><code>INL_JOIN</code></a> 进行 Index Join 操作，TiDB 会在连接外表之前创建一个中间结果的 Hash Table。TiDB 同样也支持使用 Hint <a href="/zh/tidb/stable/optimizer-hints#inl_hash_join"><code>INL_HASH_JOIN</code></a> 在外表上建 Hash Table。以上所述的 Index Join 相关算法都由 SQL 优化器自动选择。</p><h3 id="配置" style="position:relative"><a href="#%E9%85%8D%E7%BD%AE" aria-label="配置 permalink" class="anchor before"><svg aria-hidden="true" focusable="false" height="16" version="1.1" viewbox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"/></svg></a>配置</h3><p>Index Join 算法的性能受以下系统变量影响：</p><ul><li><a href="/zh/tidb/stable/system-variables#tidb_index_join_batch_size"><code>tidb_index_join_batch_size</code></a>（默认值：<code>25000</code>）- <code>index lookup join</code> 操作的 batch 大小。</li><li><a href="/zh/tidb/stable/system-variables#tidb_index_lookup_join_concurrency"><code>tidb_index_lookup_join_concurrency</code></a>（默认值：<code>4</code>）- 可以并发执行的 index lookup 任务数。</li></ul><h2 id="hash-join" style="position:relative"><a href="#hash-join" aria-label="hash join permalink" class="anchor before"><svg aria-hidden="true" focusable="false" height="16" version="1.1" viewbox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"/></svg></a>Hash Join</h2><p>在 Hash Join 操作中，TiDB 首先读取 Build 端的数据并将其缓存在 Hash Table 中，然后再读取 Probe 端的数据，使用 Probe 端的数据来探查 Hash Table 以获得所需行。与 Index Join 算法相比，Hash Join 要消耗更多内存，但如果需要连接的行数很多，运行速度会比 Index Join 快。TiDB 中的 Hash Join 算子是多线程的，并且可以并发执行。</p><p>下面是一个 Hash Join 示例：</p><pre><code class="language-sql">EXPLAIN SELECT /*+ HASH_JOIN(t1, t2) */ * FROM t1, t2 WHERE t1.id = t2.id; </code></pre><pre><code class="language-sql">+-----------------------------+-----------+-----------+---------------+------------------------------------------------+ | id | estRows | task | access object | operator info | +-----------------------------+-----------+-----------+---------------+------------------------------------------------+ | HashJoin_27 | 142020.00 | root | | inner join, equal:[eq(test.t1.id, test.t2.id)] | | ├─TableReader_29(Build) | 142020.00 | root | | data:TableFullScan_28 | | │ └─TableFullScan_28 | 142020.00 | cop[tikv] | table:t1 | keep order:false | | └─TableReader_31(Probe) | 180000.00 | root | | data:TableFullScan_30 | | └─TableFullScan_30 | 180000.00 | cop[tikv] | table:t2 | keep order:false | +-----------------------------+-----------+-----------+---------------+------------------------------------------------+ 5 rows in set (0.00 sec) </code></pre><p>TiDB 会按照以下顺序执行 <code>HashJoin_27</code> 算子：</p><ol><li>将 Build 端数据缓存在内存中。</li><li>根据缓存数据在 Build 端构造一个 Hash Table。</li><li>读取 Probe 端的数据。</li><li>使用 Probe 端的数据来探查 Hash Table。</li><li>将符合条件的结果返回给用户。</li></ol><p><code>EXPLAIN</code> 返回结果中的 <code>operator info</code> 一列记录了 <code>HashJoin_27</code> 的其他信息，包括该查询是 Inner Join 还是 Outer Join 以及 Join 的条件是什么等。在上面给出的示例中，该查询为 Inner Join，Join 条件是 <code>equal:[eq(test.t1.id, test.t2.id)]</code>，与查询语句中的 <code>WHERE t1.id = t2.id</code> 部分对应。下面例子中其他几个 Join 算子的 operator info 和此处类似。</p><h3 id="运行数据" style="position:relative"><a href="#%E8%BF%90%E8%A1%8C%E6%95%B0%E6%8D%AE" aria-label="运行数据 permalink" class="anchor before"><svg aria-hidden="true" focusable="false" height="16" version="1.1" viewbox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"/></svg></a>运行数据</h3><p>如果在执行操作时，内存使用超过了 <a href="/zh/tidb/stable/system-variables#tidb_mem_quota_query"><code>tidb_mem_quota_query</code></a> 规定的值（默认为 1 GB），且 <a href="/zh/tidb/stable/system-variables#tidb_enable_tmp_storage_on_oom"><code>tidb_enable_tmp_storage_on_oom</code></a> 的值为 <code>ON</code> （默认为 <code>ON</code>），那么 TiDB 会尝试使用临时存储，在磁盘上创建 Hash Join 的 Build 端。<code>EXPLAIN ANALYZE</code> 返回结果中的 <code>execution info</code> 一栏记录了有关内存使用情况等运行数据。下面的例子展示了 <code>tidb_mem_quota_query</code> 的值分别设为 1 GB（默认）及 500 MB 时，<code>EXPLAIN ANALYZE</code> 的返回结果（当内存配额设为 500 MB 时，磁盘用作临时存储区）：</p><pre><code class="language-sql">EXPLAIN ANALYZE SELECT /*+ HASH_JOIN(t1, t2) */ * FROM t1, t2 WHERE t1.id = t2.id; SET tidb_mem_quota_query=500 * 1024 * 1024; EXPLAIN ANALYZE SELECT /*+ HASH_JOIN(t1, t2) */ * FROM t1, t2 WHERE t1.id = t2.id; </code></pre><pre><code class="language-sql">+-----------------------------+-----------+---------+-----------+---------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------+-----------------------+---------+ | id | estRows | actRows | task | access object | execution info | operator info | memory | disk | +-----------------------------+-----------+---------+-----------+---------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------+-----------------------+---------+ | HashJoin_27 | 142020.00 | 71010 | root | | time:647.508572ms, loops:72, build_hash_table:{total:579.254415ms, fetch:566.91012ms, build:12.344295ms}, probe:{concurrency:5, total:3.23315006s, max:647.520113ms, probe:330.884716ms, fetch:2.902265344s} | inner join, equal:[eq(test.t1.id, test.t2.id)] | 209.61642456054688 MB | 0 Bytes | | ├─TableReader_29(Build) | 142020.00 | 71010 | root | | time:567.088247ms, loops:72, cop_task: {num: 2, max: 569.809411ms, min: 369.67451ms, avg: 469.74196ms, p95: 569.809411ms, max_proc_keys: 39245, p95_proc_keys: 39245, tot_proc: 400ms, rpc_num: 2, rpc_time: 939.447231ms, copr_cache_hit_ratio: 0.00} | data:TableFullScan_28 | 210.2100534439087 MB | N/A | | │ └─TableFullScan_28 | 142020.00 | 71010 | cop[tikv] | table:t1 | proc max:64ms, min:48ms, p80:64ms, p95:64ms, iters:79, tasks:2 | keep order:false | N/A | N/A | | └─TableReader_31(Probe) | 180000.00 | 90000 | root | | time:337.233636ms, loops:91, cop_task: {num: 3, max: 569.790741ms, min: 332.758911ms, avg: 421.543165ms, p95: 569.790741ms, max_proc_keys: 31719, p95_proc_keys: 31719, tot_proc: 500ms, rpc_num: 3, rpc_time: 1.264570696s, copr_cache_hit_ratio: 0.00} | data:TableFullScan_30 | 267.1126985549927 MB | N/A | | └─TableFullScan_30 | 180000.00 | 90000 | cop[tikv] | table:t2 | proc max:84ms, min:72ms, p80:84ms, p95:84ms, iters:102, tasks:3 | keep order:false | N/A | N/A | +-----------------------------+-----------+---------+-----------+---------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------+-----------------------+---------+ 5 rows in set (0.65 sec) Query OK, 0 rows affected (0.00 sec) +-----------------------------+-----------+---------+-----------+---------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------+-----------------------+----------------------+ | id | estRows | actRows | task | access object | execution info | operator info | memory | disk | +-----------------------------+-----------+---------+-----------+---------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------+-----------------------+----------------------+ | HashJoin_27 | 142020.00 | 71010 | root | | time:963.983353ms, loops:72, build_hash_table:{total:775.961447ms, fetch:503.789677ms, build:272.17177ms}, probe:{concurrency:5, total:4.805454793s, max:963.973133ms, probe:922.156835ms, fetch:3.883297958s} | inner join, equal:[eq(test.t1.id, test.t2.id)] | 93.53974533081055 MB | 210.7459259033203 MB | | ├─TableReader_29(Build) | 142020.00 | 71010 | root | | time:504.062018ms, loops:72, cop_task: {num: 2, max: 509.276857ms, min: 402.66386ms, avg: 455.970358ms, p95: 509.276857ms, max_proc_keys: 39245, p95_proc_keys: 39245, tot_proc: 384ms, rpc_num: 2, rpc_time: 911.893237ms, copr_cache_hit_ratio: 0.00} | data:TableFullScan_28 | 210.20934200286865 MB | N/A | | │ └─TableFullScan_28 | 142020.00 | 71010 | cop[tikv] | table:t1 | proc max:88ms, min:72ms, p80:88ms, p95:88ms, iters:79, tasks:2 | keep order:false | N/A | N/A | | └─TableReader_31(Probe) | 180000.00 | 90000 | root | | time:363.058382ms, loops:91, cop_task: {num: 3, max: 412.659191ms, min: 358.489688ms, avg: 391.463008ms, p95: 412.659191ms, max_proc_keys: 31719, p95_proc_keys: 31719, tot_proc: 484ms, rpc_num: 3, rpc_time: 1.174326746s, copr_cache_hit_ratio: 0.00} | data:TableFullScan_30 | 267.11340618133545 MB | N/A | | └─TableFullScan_30 | 180000.00 | 90000 | cop[tikv] | table:t2 | proc max:92ms, min:64ms, p80:92ms, p95:92ms, iters:102, tasks:3 | keep order:false | N/A | N/A | +-----------------------------+-----------+---------+-----------+---------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------+-----------------------+----------------------+ 5 rows in set (0.98 sec) </code></pre><h3 id="配置-1" style="position:relative"><a href="#%E9%85%8D%E7%BD%AE-1" aria-label="配置 1 permalink" class="anchor before"><svg aria-hidden="true" focusable="false" height="16" version="1.1" viewbox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"/></svg></a>配置</h3><p>Hash Join 算法的性能受以下系统变量影响：</p><ul><li><a href="/zh/tidb/stable/system-variables#tidb_mem_quota_query"><code>tidb_mem_quota_query</code></a>（默认值：1GB）- 如果某条查询的内存消耗超出了配额，TiDB 会尝试将 Hash Join 的 Build 端移到磁盘上以节省内存。</li><li><a href="/zh/tidb/stable/system-variables#tidb_hash_join_concurrency"><code>tidb_hash_join_concurrency</code></a>（默认值：<code>5</code>）- 可以并发执行的 Hash Join 任务数量。</li></ul><h2 id="merge-join" style="position:relative"><a href="#merge-join" aria-label="merge join permalink" class="anchor before"><svg aria-hidden="true" focusable="false" height="16" version="1.1" viewbox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"/></svg></a>Merge Join</h2><p>Merge Join 是一种特殊的 Join 算法。当两个关联表要 Join 的字段需要按排好的顺序读取时，适用 Merge Join 算法。由于 Build 端和 Probe 端的数据都会读取，这种算法的 Join 操作是流式的，类似“拉链式合并”的高效版。Merge Join 占用的内存要远低于 Hash Join，但 Merge Join 不能并发执行。</p><p>下面是一个使用 Merge Join 的例子：</p><pre><code class="language-sql">EXPLAIN SELECT /*+ MERGE_JOIN(t1, t2) */ * FROM t1, t2 WHERE t1.id = t2.id; </code></pre><pre><code class="language-sql">+-----------------------------+-----------+-----------+---------------+-------------------------------------------------------+ | id | estRows | task | access object | operator info | +-----------------------------+-----------+-----------+---------------+-------------------------------------------------------+ | MergeJoin_7 | 142020.00 | root | | inner join, left key:test.t1.id, right key:test.t2.id | | ├─TableReader_12(Build) | 180000.00 | root | | data:TableFullScan_11 |