Sergei Petrunia's blog

MySQL 5.6 added support for EXPLAIN INSERT/UPDATE/DELETE. MySQL 5.7 made some improvements to EXPLAIN code. Also, 5.7 added support for EXPLAIN FOR CONNECTION, which looks very similar to MariaDB’s SHOW EXPLAIN. I was working on putting EXPLAIN INSERT/UPDATE/DELETE into MariaDB 10.0, so I’ve dug up some details and thought I’d share them:

EXPLAIN UPDATE/DELETE

EXPLAIN UPDATE looks like regular EXPLAIN SELECT:

MySQL [dbt3sf1]> explain update orders set o_orderpriority='1-URGENT' where o_orderDATE='1998-07-21';
+----+-------------+--------+-------+---------------+---------------+---------+-------+------+-------------+
| id | select_type | table  | type  | possible_keys | key           | key_len | ref   | rows | Extra       |
+----+-------------+--------+-------+---------------+---------------+---------+-------+------+-------------+
|  1 | SIMPLE      | orders | range | i_o_orderdate | i_o_orderdate | 4       | const |  628 | Using where |
+----+-------------+--------+-------+---------------+---------------+---------+-------+------+-------------+

EXPLAIN PARTITIONS shows partitions. EXPLAIN EXTENDED shows the “filtered” column, but doesn’t produce a warning with query after rewrites.

In MySQL 5.7, select_type changes from SIMPLE to UPDATE.

MySQL [dbt3sf1]> explain update orders set o_orderpriority='3-MEDIUM' where o_orderDATE='1998-07-21';
+----+-------------+--------+-------+---------------+---------------+---------+-------+------+-------------+
| id | select_type | table  | type  | possible_keys | key           | key_len | ref   | rows | Extra       |
+----+-------------+--------+-------+---------------+---------------+---------+-------+------+-------------+
|  1 | UPDATE      | orders | range | i_o_orderdate | i_o_orderdate | 4       | const |  628 | Using where |
+----+-------------+--------+-------+---------------+---------------+---------+-------+------+-------------+

for multi-table UPDATEs (or DELETEs), select_type=UPDATE (or DELETE) only for tables that are being updated.

Inside, there are some issues. Best-release-ever still suffers from an old MySQL design where execution path taken by EXPLAIN is different from execution path taken by the actual query. If you have something in two places, it goes out of sync in no time, and I have alreay found one example EXPLAIN will show “Using join buffer” where the execution doesn’t use it (filed as BUG#70553). A shameless plug: in MariaDB 10.0, we use one execution path, so bugs like this one are not possible.

Another peculiarity is Using temporary. You can run a query like this:

MySQL [dbt3sf1]> explain update orders set o_orderDATE='1998-07-22' where o_orderDATE='1998-07-21';
+----+-------------+--------+-------+---------------+---------------+---------+-------+------+------------------------------+
| id | select_type | table  | type  | possible_keys | key           | key_len | ref   | rows | Extra                        |
+----+-------------+--------+-------+---------------+---------------+---------+-------+------+------------------------------+
|  1 | UPDATE      | orders | range | i_o_orderdate | i_o_orderdate | 4       | const |  628 | Using where; Using temporary |
+----+-------------+--------+-------+---------------+---------------+---------+-------+------+------------------------------+

and see Using temporary. This looks weird, why would this query need a temporary table? Running the UPDATE and checking Created_tmp_tables shows that indeed no temporary tables were created. The reason for “Using temporary” is that the UPDATE modifies columns in the index that is used to find rows. When that happens, the optimizer needs to collect record ids in a buffer before doing any updates. But, records are collected in a buffer, not in a temporary table. We in MariaDB decided to denote this with Using buffer instead:

MariaDB [dbt3sf1]> explain update orders set o_orderDATE='1998-07-22' where o_orderDATE='1998-07-21';
+------+-------------+--------+-------+---------------+---------------+---------+------+------+---------------------------+
| id   | select_type | table  | type  | possible_keys | key           | key_len | ref  | rows | Extra                     |
+------+-------------+--------+-------+---------------+---------------+---------+------+------+---------------------------+
|    1 | SIMPLE      | orders | range | i_o_orderdate | i_o_orderdate | 4       | NULL |  628 | Using where; Using buffer |
+------+-------------+--------+-------+---------------+---------------+---------+------+------+---------------------------+

EXPLAIN INSERT

This one is weird – INSERT doesn’t have a query plan, why would one need EXPLAIN for it? One could argue that EXPLAIN INSERT ... SELECT makes sense, as the SELECT part has a query plan. However, using that logic, EXPLAIN CREATE TABLE … SELECT should have been supported also, but it isn’t.

In MySQL 5.6, EXPLAIN INSERT seems to be totally useless:

MySQL [dbt3sf1]> explain insert into orders (o_custkey) values ('32423');
+----+-------------+-------+------+---------------+------+---------+------+------+----------------+
| id | select_type | table | type | possible_keys | key  | key_len | ref  | rows | Extra          |
+----+-------------+-------+------+---------------+------+---------+------+------+----------------+
|  1 | SIMPLE      | NULL  | NULL | NULL          | NULL | NULL    | NULL | NULL | No tables used |
+----+-------------+-------+------+---------------+------+---------+------+------+----------------+

In MySQL 5.7, it shows the table that we insert into:

MySQL [dbt3sf1]> explain insert into orders (o_custkey) values ('32423');
+----+-------------+--------+------+---------------+------+---------+------+------+-------+
| id | select_type | table  | type | possible_keys | key  | key_len | ref  | rows | Extra |
+----+-------------+--------+------+---------------+------+---------+------+------+-------+
|  1 | INSERT      | orders | ALL  | NULL          | NULL | NULL    | NULL | NULL | NULL  |
+----+-------------+--------+------+---------------+------+---------+------+------+-------+

“type=ALL” is misleading, because no full table scan will take place. The only scenario when EXPLAIN INSERT can be useful is when one is doing an INSERT into partitioned table. In that case, EXPLAIN PARTITIONS will show the result of partition pruning. Note that partition pruning for INSERTs works in a rather peculiar way – depending on the number of rows inserted, it may choose not to perform partition pruning and will show that all partitions will be used.

I think this post is getting long, so I will talk about SHOW EXPLAIN and EXPLAIN FOR CONNECTION in a separate post.

Valeriy has mentioned that MySQL manual has no documentation about EXPLAIN FORMAT=JSON feature. There was a bug filed for this since September, 2012. I had spent some time looking at EXPLAIN FORMAT=JSON, so I thought I would share my findings.

The first blog post about EXPLAIN FORMAT=JSON was made by Sheeri. She listed these features of FORMAT=JSON:

• attached_condition and implicit casts
• used_key_parts field

In addition to those, I was able to find

• Pushed index condition information
• More information about sorting/grouping
• Subquery attachment points information

Let’s look at these in greater detail:

Pushed index condition information

This is very similar to conditions attached to tables – instead of “Using index condition” one can see the pushed condition itself:

explain format=json select * from Country where Name like 'p%' and Population > 3*1000*1000;
  "query_block": {
    "select_id": 1,
    "table": {
      "table_name": "Country",
      "access_type": "range",
      "possible_keys": [
        "Name",
        "Population"
      ],
      "key": "Name",
      "used_key_parts": [
        "Name"
      ],
      "key_length": "52",
      "rows": 12,
      "filtered": 100,
      "index_condition": "(`world`.`Country`.`Name` like 'p%')",
       "attached_condition": "(`world`.`Country`.`Population` > (((3 * 1000) * 1000)))" 
    }
  }

This is very useful. I wish the condition was printed without the over-quoting of every identifier.

More information about sorting/grouping

The old “Using temporary; Using filesort” was replaced with ordering_operation and grouping_operation nodes. The nodes always have using_filesort attribute, and may optionally have using_temporary_table attribute.

explain format=json select * from City order by Population desc limit 1;
*************************** 1. row ***************************
EXPLAIN: {
  "query_block": {
    "select_id": 1,
    "ordering_operation": {
      "using_filesort": true,
      "table": {
        "table_name": "City",
        "access_type": "ALL",
        "rows": 4249,
        "filtered": 100
      }
    }
  }
}

This provides slightly more information than the old “Using temporary; Using filesort” line. In the following example, one can see that “Using temporary” is done to handle GROUP BY, while filesort is used for GROUP BY:

explain format=json
select sum(population) as city_ppl, Country
from City
group by Country 
order by city_ppl desc;
*************************** 1. row ***************************
EXPLAIN: {
  "query_block": {
    "select_id": 1,
    "ordering_operation": {
      "using_filesort": true,
      "grouping_operation": {
        "using_temporary_table": true,
        "using_filesort": false,
        "table": {
          "table_name": "City",
          "access_type": "ALL",
          "rows": 4409,
          "filtered": 100
        }
      }
    }
  }
}

It is also possible to encounter ordering_operation with using_filesort:false, which means that the optimizer relies on the query plan to produce the data in required ordering.

Subquery attachment points

EXPLAIN FORMAT=JSON shows where the subqueries are attached to queries. In tabular form, you see something like this:

+----+--------------------+-----------+------+---------------+------+---------+------+------+-------------+
| id | select_type        | table     | type | possible_keys | key  | key_len | ref  | rows | Extra       |
+----+--------------------+-----------+------+---------------+------+---------+------+------+-------------+
|  1 | PRIMARY            | Country   | ALL  | Name          | NULL | NULL    | NULL |  165 | Using where |
|  3 | SUBQUERY           | satellite | ALL  | NULL          | NULL | NULL    | NULL | 1041 | NULL        |
|  2 | DEPENDENT SUBQUERY | City      | ALL  | NULL          | NULL | NULL    | NULL | 4409 | Using where |
+----+--------------------+-----------+------+---------------+------+---------+------+------+-------------+

and it is not apparent how subqueries are attached to queries. For basic queries, this is not a problem – just look at the query. However, if the query has VIEWs or FROM clause subqueries, it may be difficult to recognize the query plan structure. In any case, with EXPLAIN FORMAT=JSON, subqueries look like this:

*************************** 1. row ***************************
EXPLAIN: {
  "query_block": {
    "select_id": 1,
    "table": {
      "table_name": "Country",
      "access_type": "range",
      "possible_keys": [
        "Name"
      ],
      "key": "Name",
      "used_key_parts": [
        "Name"
      ],
      "key_length": "52",
      "rows": 2,
      "filtered": 100,
      "index_condition": "(`world`.`Country`.`Name` in ('France','Germany'))"
    },
    "select_list_subqueries": [
      {
        "dependent": true,
        "cacheable": false,
        "query_block": {
          "select_id": 2,
          "table": {
            "table_name": "City",
            "access_type": "ALL",
            "rows": 4409,
            "filtered": 100,
            "attached_condition": "(`world`.`City`.`Country` = `world`.`Country`.`Code`)"
          }
        }
      }
    ]
  }
}

which makes it more apparent where the subquery is attached and how often it is re-executed.

My personal impressions

EXPLAIN FORMAT=JSON looks like a project that was started with good intents but wasn’t finished. I’ve found a couple of bugs by just playing around (1, 2). A number of things look out of place:

• “Range checked for each record” still displays “index map: 0xNNNN”, leaving one to do binary arithmetic with index numbers to figure out which indexes will be checked
• We’ve got used_key_parts. Is there any need to print key_length anymore?
• index_merge is still described with notation like key=”intersect(key1,key2)”, and key_length=”5,5″ (where’s the used_key_parts for it?). This notation was the only way to fit the data into tabular format. With JSON, it would be natural to represent individual index scans as separate JSON objects
• If a subquery is part of WHERE clause, attached_condition will print the whole subquery. That is, subquery’s WHERE condition is printed both in the parent query and in the subquery. This is redundant and annoying.
• Items with default value are generally not printed. For example, possible_keys=NULL is not. However, using_filesort:false is printed, and so is filtered:100, which means no filtering happened. Why?

Overall, I don’t quite understand what EXPLAIN FORMAT=JSON was targeted at. If EXPLAIN FORMAT=JSON was targeted at humans, I would expect a more user-friendly output. Don’t print redundant information, don’t do redundant quoting, do a better job at indenting the JSON output.
If EXPLAIN FORMAT=JSON was targeted at machines, I would expect it to be fully documented. How can one expect somebody to write programs to process a data format that has no documentation?

I’ve put online the slides for the two talks that I сo-presented at the Percona Live conference:

• Advanced query optimizer tuning and analysis tutorial (slideshare, pdf)
• MariaDB Cassandra interoperability session (slideshare, pdf)

The tutorial tries to cover most areas of the optimizer, with focus being on optimization of complex parts of SQL like joins. It also shows how recent additions in MySQL and MariaDB (PERFORMANCE_SCHEMA, SHOW EXPLAIN, EXPLAIN FORMAT=JSON, optimizer_trace) can be useful when diagnosing optimizer problems.

The Cassandra talk starts with an introduction into Cassandra and Cassandra Storage Engine, and then proceeds to discuss the intended use cases for Cassandra Storage Engine. This is an important discussion, because there is a variety of reasons one might want to access Cassandra’s data from SQL, and Cassandra Storage Engine addresses only certain kinds of needs.