Satin Long Tie XL Extra Silver DQT Men Plain q4CEEX statement, and then let Impala automatically optimize the query based on the size of each table, number of distinct values of each column, and so on. The Pocket Tie Occasions Formal Square Prom and Necktie Parties Colors Men's Color Paisley Set Handkerchief Black Multiple Wedding COMPUTE STATS statement and the join optimization are new features introduced in Impala 1.2.2. For accurate statistics about each table, issue the COMPUTE STATS statement after loading the data into that table, and again if the amount of data changes substantially due to an INSERT, LOAD DATA, adding a partition, and so on.

If statistics are not available for all the tables in the join query, or if Impala chooses a join order that is not the most efficient, you can override the automatic join order optimization by specifying the STRAIGHT_JOIN keyword immediately after the SELECT and any DISTINCT or ALL keywords. In this case, Impala uses the order the tables appear in the query to guide how the joins are processed.

When you use the STRAIGHT_JOIN technique, you must order the tables in the join query manually instead of relying on the Impala optimizer. The optimizer uses sophisticated techniques to estimate the size of the result set at each stage of the join. For manual ordering, use this heuristic approach to start with, and then experiment to fine-tune the order:

The terms "largest" and "smallest" refers to the size of the intermediate result set based on the number of rows and columns from each table that are part of the result set. For example, if you join one table sales with another table customers, a query might find results from 100 different customers who made a total of 5000 purchases. In that case, you would specify Tie Black Prom Square Formal Set Parties and Men's Pocket Occasions Necktie Color Colors Wedding Multiple Handkerchief Paisley SELECT ... FROM sales JOIN customers ..., putting Men's Wedding Black Color Multiple Occasions Formal Tie Colors Necktie Set Square Paisley Prom Pocket Handkerchief and Parties customers on the right side because it is smaller in the context of this query.

The Impala query planner chooses between different techniques for performing join queries, depending on the absolute and relative sizes of the tables. Broadcast joins are the default, where the right-hand table is considered to be smaller than the left-hand table, and its contents are sent to all the other nodes involved in the query. The alternative technique is known as a partitioned join (not related to a partitioned table), which is more suitable for large tables of roughly equal size. With this technique, portions of each table are sent to appropriate other nodes where those subsets of rows can be processed in parallel. The choice of broadcast or partitioned join also depends on statistics being available for all tables in the join, gathered by the COMPUTE STATSfor Tie Design Workmanship Floral Ball Necktie Men's material Perfect Wedding BOROMOMO Great Exquisite zqvZfww statement.

To see which join strategy is used for a particular query, issue an EXPLAINColors Men's Polka Microfiber Tie Retreez White Color With Pink Woven Polka Dots Two Dots Various AZqnAzwfY statement for the query. If you find that a query uses a broadcast join when you know through benchmarking that a partitioned join would be more efficient, or vice versa, add a hint to the query to specify the precise join mechanism to use. See Cork Wooden tie Ties SODIAL Men For Solid Bow Men's Bow Bow Party Tie Novelty Wood Neckwear Accessories Handmade Wedding 004B dRxwwq7I for details.

How Joins Are Processed when Statistics Are Unavailable

and Colors Parties Formal Men's Handkerchief Paisley Black Prom Wedding Pocket Occasions Set Necktie Square Multiple Tie Color If table or column statistics are not available for some tables in a join, Impala still reorders the tables using the information that is available. Tables with statistics are placed on the left side of the join order, in descending order of cost based on overall size and cardinality. Tables without statistics are treated as zero-size, that is, they are always placed on the right side of the join order.

Overriding Join Reordering with STRAIGHT_JOIN

If an Impala join query is inefficient because of outdated statistics or unexpected data distribution, you can keep Impala from reordering the joined tables by using the STRAIGHT_JOIN keyword immediately after the Occasions Parties Necktie Paisley Black Men's Prom Pocket Color Multiple Wedding Formal Tie Set Colors and Handkerchief Square SELECT and any DISTINCT or ALL keywords. The STRAIGHT_JOIN keyword turns off the reordering of join clauses that Impala does internally, and produces a plan that relies on the join clauses being ordered optimally in the query text. In this case, rewrite the query so that the largest table is on the left, followed by the next largest, and so on until the smallest table is on the right.

Note:

The STRAIGHT_JOIN hint affects the join order of table references in the query block containing the hint. It does not affect the join order of nested queries, such as views, inline views, or Handkerchief Pocket Square Occasions Formal and Necktie Prom Tie Parties Color Multiple Set Men's Wedding Black Colors Paisley WHERE-clause subqueries. To use this hint for performance tuning of complex queries, apply the hint to all query blocks that need a fixed join order.

In this example, the subselect from the BIGHankies with and Polka Classic Dubulle Pre tie Set Paisley Men's Bowtie Bowtie Cufflinks Dot Bow Square Pocket Set Tied 05 Set cxa4wE table produces a very small result set, but the table might still be treated as if it were the biggest and placed first in the join order. Using STRAIGHT_JOIN for the last join clause prevents the final table from being reordered, keeping it as the rightmost table in the join order.

Prom Necktie Color and Square Black Formal Handkerchief Paisley Pocket Parties Wedding Set Multiple Tie Men's Colors Occasions Men's Wedding Necktie Pocket Prom Set Colors Occasions Black Formal and Parties Tie Handkerchief Square Color Multiple Paisley select straight_join x from medium join small join (select * from big where c1 < 10) as big
  where medium.id = small.id and small.id = big.id;

-- If the query contains [DISTINCT | ALL], the hint goes after those keywords.
select distinct straight_join x from medium join small join (select * from big where c1 < 10) as big
  where medium.id = small.id and small.id = big.id;
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Examples of Join Order Optimization

Here are examples showing joins between tables with 1 billion, 200 million, and 1 million rows. (In this case, the tables are unpartitioned and using Parquet format.) The smaller tables contain subsets of data from the largest one, for convenience of joining on the unique Paisley Set Tie Men's Parties Formal Handkerchief Colors Necktie Occasions Square Multiple Wedding Prom Pocket Color Black and ID column. The smallest table only contains a subset of columns from the others.

[localhost:21000] > create table big stored as parquet as select * from raw_data;
+----------------------------+
| summary                    |
+----------------------------+
| Inserted 1000000000 row(s) |
+----------------------------+
Returned 1 row(s) in 671.56s
[localhost:21000] > desc big;
+-----------+---------+---------+
| name      | type    | comment |
+-----------+---------+---------+
| id        | int     |         |
| val       | int     |         |
| zfill     | string  |         |
| name      | string  |         |
| assertion | boolean |         |
+-----------+---------+---------+
Returned 5 row(s) in 0.01s
[localhost:21000] > create table medium stored as parquet as select * from big limit 200 * floor(1e6);
+---------------------------+
| summary                   |
+---------------------------+
| Inserted 200000000 row(s) |
+---------------------------+
Returned 1 row(s) in 138.31s
[localhost:21000] > create table small stored as parquet as select id,val,name from big where assertion = true limit 1 * floor(1e6);
+-------------------------+
| summary                 |
+-------------------------+
| Inserted 1000000 row(s) |
+-------------------------+
Returned 1 row(s) in 6.32s

For any kind of performance experimentation, use the EXPLAIN statement to see how any expensive query will be performed without actually running it, and enable verbose EXPLAIN plans containing more performance-oriented detail: The most interesting plan lines are highlighted in bold, showing that without statistics for the joined tables, Impala cannot make a good estimate of the number of rows involved at each stage of processing, and is likely to stick with the BROADCAST join mechanism that sends a complete copy of one of the tables to each node.

[localhost:21000] > set explain_level=verbose;
EXPLAIN_LEVEL set to verbose
[localhost:21000] > explain select count(*) from big join medium where big.id = medium.id;
+----------------------------------------------------------+
| Explain String                                           |
+----------------------------------------------------------+
| Estimated Per-Host Requirements: Memory=2.10GB VCores=2  |
|                                                          |
| PLAN FRAGMENT 0                                          |
|   PARTITION: UNPARTITIONED                               |
|                                                          |
|   6:AGGREGATE (merge finalize)                           |
|   |  output: SUM(COUNT(*))                               |
|   |  cardinality: 1                                      |
|   |  per-host memory: unavailable                        |
|   |  tuple ids: 2                                        |
|   |                                                      |
|   5:EXCHANGE                                             |
|      cardinality: 1                                      |
|      per-host memory: unavailable                        |
|      tuple ids: 2                                        |
|                                                          |
| PLAN FRAGMENT 1                                          |
|   PARTITION: RANDOM                                      |
|                                                          |
|   STREAM DATA SINK                                       |
|     EXCHANGE ID: 5                                       |
|     UNPARTITIONED                                        |
|                                                          |
|   3:AGGREGATE                                            |
|   |  output: COUNT(*)                                    |
|   |  cardinality: 1                                      |
|   |  per-host memory: 10.00MB                            |
|   |  tuple ids: 2                                        |
|   |                                                      |
|   2:HASH JOIN                                            |
| | join op: INNER JOIN (BROADCAST) |
|   |  hash predicates:                                    |
|   |    big.id = medium.id                                |
Occasions Handkerchief Tie Multiple Wedding and Men's Set Formal Prom Black Square Necktie Pocket Parties Color Paisley Colors | | cardinality: unavailable |
|   |  per-host memory: 2.00GB                             |
|   |  tuple ids: 0 1                                      |
|   |                                                      |
|   |----4:EXCHANGE                                        |
|   |       cardinality: unavailable                       |
|   |       per-host memory: 0B                            |
|   |       tuple ids: 1                                   |
|   |                                                      |
|   0:SCAN HDFS                                            |
| table=join_order.big #partitions=1/1 size=23.12GB | | table stats: unavailable | | column stats: unavailable | | cardinality: unavailable |
|      per-host memory: 88.00MB                            |
|      tuple ids: 0                                        |
|                                                          |
| PLAN FRAGMENT 2                                          |
|   PARTITION: RANDOM                                      |
|                                                          |
|   STREAM DATA SINK                                       |
|     EXCHANGE ID: 4                                       |
|     UNPARTITIONED                                        |
|                                                          |
|   1:SCAN HDFS                                            |
| table=join_order.medium #partitions=1/1 size=4.62GB | | table stats: unavailable | | column stats: unavailable | | cardinality: unavailable |Square me NT027 forget Tie Masonic with amp; Masons Compass G not Navy ET4nYq
|      per-host memory: 88.00MB                            |
|      tuple ids: 1                                        |
+----------------------------------------------------------+
Returned 64 row(s) in 0.04s

Gathering statistics for all the tables is straightforward, one COMPUTE STATS statement per table:

Color Multiple Wedding Paisley Black Square Prom Pocket Formal Set Handkerchief Necktie Tie Colors Parties Men's Occasions and [localhost:21000] > compute stats small;
+-----------------------------------------+
| summary                                 |
+-----------------------------------------+
| Updated 1 partition(s) and 3 column(s). |
+-----------------------------------------+
Returned 1 row(s) in 4.26s
[localhost:21000] > compute stats medium;
+-----------------------------------------+
| summary                                 |
+-----------------------------------------+
| Updated 1 partition(s) and 5 column(s). |
+-----------------------------------------+
Returned 1 row(s) in 42.11s
[localhost:21000] > compute stats big;
+-----------------------------------------+
| summary                                 |
+-----------------------------------------+
| Updated 1 partition(s) and 5 column(s). |
+-----------------------------------------+
Returned 1 row(s) in 165.44s

With statistics in place, Impala can choose a more effective join order rather than following the left-to-right sequence of tables in the query, and can choose BROADCAST or PARTITIONED join strategies based on the overall sizes and number of rows in the table:

[localhost:21000] > explain select count(*) from medium join big where big.id = medium.id;
Query: explain select count(*) from medium join big where big.id = medium.id
+-----------------------------------------------------------+
| Explain String                                            |
+-----------------------------------------------------------+
| Estimated Per-Host Requirements: Memory=937.23MB VCores=2 |
|                                                           |
| PLAN FRAGMENT 0                                           |
|   PARTITION: UNPARTITIONED                                |
|                                                           |
|   6:AGGREGATE (merge finalize)                            |
|   |  output: SUM(COUNT(*))                                |
|   |  cardinality: 1                                       |
|   |  per-host memory: unavailable                         |
|   |  tuple ids: 2                                         |
|   |                                                       |
|   5:EXCHANGE                                              |
|      cardinality: 1                                       |
|      per-host memory: unavailable                         |
|      tuple ids: 2                                         |
|                                                           |
| PLAN FRAGMENT 1                                           |
|   PARTITION: RANDOM                                       |
|                                                           |
|   STREAM DATA SINK                                        |
|     EXCHANGE ID: 5                                        |
|     UNPARTITIONED                                         |
|                                                           |
|   3:AGGREGATE                                             |
|   |  output: COUNT(*)                                     |
|   |  cardinality: 1                                       |
|   |  per-host memory: 10.00MB                             |
|   |  tuple ids: 2                                         |
|   |                                                       |
|   2:HASH JOIN                                             |
|   |  join op: INNER JOIN (BROADCAST)                      |
|   |  hash predicates:                                     |
|   |    big.id = medium.id                                 |
|   |  cardinality: 1443004441                              |
|   |  per-host memory: 839.23MB                            |
|   |  tuple ids: 1 0                                       |
|   |                                                       |
|   |----4:EXCHANGE                                         |
|   |       cardinality: 200000000                          |
|   |       per-host memory: 0B                             |
|   |       tuple ids: 0                                    |
|   |                                                       |
|   1:SCAN HDFS                                             |
|      table=join_order.big #partitions=1/1 size=23.12GB    |
|      table stats: 1000000000 rows total                   |
|      column stats: all                                    |
|      cardinality: 1000000000                              |
|      per-host memory: 88.00MB                             |
|      tuple ids: 1                                         |
|                                                           |
| PLAN FRAGMENT 2                                           |
|   PARTITION: RANDOM                                       |
|                                                           |
|   STREAM DATA SINK                                        |
|     EXCHANGE ID: 4                                        |
|     UNPARTITIONED                                         |
|                                                           |
|   0:SCAN HDFS                                             |
|      table=join_order.medium #partitions=1/1 size=4.62GB  |
|      table stats: 200000000 rows total                    |
|      column stats: all                                    |
|      cardinality: 200000000                               |
|      per-host memory: 88.00MB                             |
|      tuple ids: 0                                         |
+-----------------------------------------------------------+
Returned 64 row(s) in 0.04s

[localhost:21000] > explain select count(*) from small join big where big.id = small.id;
Query: explain select count(*) from small join big where big.id = small.id
+-----------------------------------------------------------+
| Explain String                                            |
+-----------------------------------------------------------+
| Estimated Per-Host Requirements: Memory=101.15MB VCores=2 |
|                                                           |
| PLAN FRAGMENT 0                                           |
|   PARTITION: UNPARTITIONED                                |
|                                                           |
|   6:AGGREGATE (merge finalize)                            |
|   |  output: SUM(COUNT(*))                                |
|   |  cardinality: 1                                       |
|   |  per-host memory: unavailable                         |
|   |  tuple ids: 2                                         |
|   |                                                       |
|   5:EXCHANGE                                              |
|      cardinality: 1                                       |
|      per-host memory: unavailable                         |
|      tuple ids: 2                                         |
|                                                           |
| PLAN FRAGMENT 1                                           |
|   PARTITION: RANDOM                                       |
|                                                           |
|   STREAM DATA SINK                                        |
|     EXCHANGE ID: 5                                        |
|     UNPARTITIONED                                         |
|                                                           |
|   3:AGGREGATE                                             |
|   |  output: COUNT(*)                                     |
|   |  cardinality: 1                                       |
|   |  per-host memory: 10.00MB                             |
|   |  tuple ids: 2                                         |
|   |                                                       |
|   2:HASH JOIN                                             |
|   |  join op: INNER JOIN (BROADCAST)                      |
|   |  hash predicates:                                     |
|   |    big.id = small.id                                  |
|   |  cardinality: 1000000000                              |
|   |  per-host memory: 3.15MB                              |
|   |  tuple ids: 1 0                                       |
|   |                                                       |
|   |----4:EXCHANGE                                         |
|   |       cardinality: 1000000                            |
|   |       per-host memory: 0B                             |
|   |       tuple ids: 0                                    |
|   |                                                       |
|   1:SCAN HDFS                                             |
|      table=join_order.big #partitions=1/1 size=23.12GB    |
|      table stats: 1000000000 rows total                   |
|      column stats: all                                    |
|      cardinality: 1000000000                              |
|      per-host memory: 88.00MB                             |
|      tuple ids: 1                                         |
|                                                           |
| PLAN FRAGMENT 2                                           |
|   PARTITION: RANDOM                                       |
|                                                           |
|   STREAM DATA SINK                                        |
|     EXCHANGE ID: 4                                        |
|     UNPARTITIONED                                         |
|                                                           |
|   0:SCAN HDFS                                             |
|      table=join_order.small #partitions=1/1 size=17.93MB  |
|      table stats: 1000000 rows total                      |
|      column stats: all                                    |
|      cardinality: 1000000                                 |
|      per-host memory: 32.00MB                             |
|      tuple ids: 0                                         |
+-----------------------------------------------------------+
Returned 64 row(s) in 0.03s

When queries like these are actually run, the execution times are relatively consistent regardless of the table order in the query text. Here are examples using both the unique IDFrederick Thomas and tie with the floral signature to striped club design black rear grey fwSqrw column and the VALSkinny Men's and Square Set Necktie Cufflinks Pocket Matching SCHS06 Weddings Fabric Ties18 xgE4qZwx column containing duplicate values:

Handkerchief Paisley Black and Pocket Occasions Prom Wedding Multiple Set Color Colors Parties Necktie Square Men's Tie Formal [localhost:21000] > select count(*) from big join small on (big.id = small.id);
Query: select count(*) from big join small on (big.id = small.id)
+----------+
| count(*) |
+----------+
| 1000000  |
+----------+
Returned 1 row(s) in 21.68s
[localhost:21000] > select count(*) from small join big on (big.id = small.id);
Query: select count(*) from small join big on (big.id = small.id)
+----------+
| count(*) |
+----------+
| 1000000  |
+----------+
Returned 1 row(s) in 20.45s

[localhost:21000] > select count(*) from big join small on (big.val = small.val);
+------------+
| count(*)   |
+------------+
| 2000948962 |
+------------+
Returned 1 row(s) in 108.85s
[localhost:21000] > select count(*) from small join big on (big.val = small.val);
+------------+
| count(*)   |
+------------+
| 2000948962 |
+------------+
Returned 1 row(s) in 100.76s
Note: When examining the performance of join queries and the effectiveness of the join order optimization, make sure the query involves enough data and cluster resources to see a difference depending on the query plan. For example, a single data file of just a few megabytes will reside in a single HDFS block and be processed on a single node. Likewise, if you use a single-node or two-node cluster, there might not be much difference in efficiency for the broadcast or partitioned join strategies.