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Analysis on Group by and Order by Performance

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Analysis on Group by and Order by Performance

An analysis of some common issues involving 'group by'; and 'order by' performance.

· Performance Zone ·
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Recently I found it very slow to perform a sorting task in a log table. After the problem was finally solved, I summarized some experience in the index and MySQL execution process; however, I still have five unsolved problems and I hope that you can help me solve these problems.

These problems are related to the following:

  • Using data to show how the number of scanned rows in a slow query log is obtained
  • Finding a solution based on the principle of "group by"
  • Checking implementation details of sorting
  • Debugging source code with gdb

Background

List the top 10 visited articles of this month and week respectively. The log table is as follows.

CREATE TABLE `article_rank` (
  `id` int(11) unsigned NOT NULL AUTO_INCREMENT,
  `aid` int(11) unsigned NOT NULL,
  `pv` int(11) unsigned NOT NULL DEFAULT '1',
  `day` int(11) NOT NULL COMMENT '日期 例如 20171016',
  PRIMARY KEY (`id`),
  KEY `idx_day_aid_pv` (`day`,`aid`,`pv`),
  KEY `idx_aid_day_pv` (`aid`,`day`,`pv`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8

Preparations

To verify the guesses, install a debugger for MySQL and enable slow query log collection to count scanned rows.

Installation

  • Download the source code
  • Compile the installation
  • Create a MySQL user
  • Initialize the database
  • Initialize the MySQL configuration file
  • Change the password

Enable Slow Query Log

Edit the configuration file and add the following under the [mysqld] module:

slow_query_log=1
slow_query_log_file=xxx
long_query_time=0
log_queries_not_using_indexes=1

Performance Analysis


Discovering the Problem

Assume that the following SQL statement is used to query the top 10 articles by views during the five days from 2018-12-20 to 2018-12-24. Let's first use explain to see the analysis result.

mysql> explain select aid,sum(pv) as num from article_rank where day>=20181220 and day<=20181224 group by aid order by num desc limit 10;
+----+-------------+--------------+------------+-------+-------------------------------+----------------+---------+------+--------+----------+-----------------------------------------------------------+
| id | select_type | table        | partitions | type  | possible_keys                 | key            | key_len | ref  | rows   | filtered | Extra                                                     |
+----+-------------+--------------+------------+-------+-------------------------------+----------------+---------+------+--------+----------+-----------------------------------------------------------+
|  1 | SIMPLE      | article_rank | NULL       | range | idx_day_aid_pv,idx_aid_day_pv | idx_day_aid_pv | 4       | NULL | 404607 |   100.00 | Using where; Using index; Using temporary; Using filesort |
+----+-------------+--------------+------------+-------+-------------------------------+----------------+---------+------+--------+----------+-----------------------------------------------------------+

By default, the index idx_day_aid_pv is used. The Extra shows that the covering index is used when we use idx_day_aid_pv. However, a temporary table will be required and this involves sorting.

Let's check the information recorded in the slow query log.

# Time: 2019-03-17T03:02:27.984091Z
# User@Host: root[root] @ localhost []  Id:     6
# Query_time: 56.959484  Lock_time: 0.000195 Rows_sent: 10  Rows_examined: 1337315
SET timestamp=1552791747;
select aid,sum(pv) as num from article_rank where day>=20181220 and day<=20181224 group by aid order by num desc limit 10;

Why Were 1,337,315 Rows Scanned?

We will query two data items: the number of matching rows and the number of rows after the group byoperation.

mysql> select count(*) from article_rank where day>=20181220 and day<=20181224;
+----------+
| count(*) |
+----------+
|   785102 |
+----------+

mysql> select count(distinct aid) from article_rank where day>=20181220 and day<=20181224;
+---------------------+
| count(distinct aid) |
+---------------------+
|              552203 |
+---------------------+

The result indicates that the value of Rows_examined in the slow query log is the total of the matching rows (785,102), the number of rows after group by (552,203), and the limit value.

To explain the preceding finding, we need to understand how this SQL statement runs.

Performing Process Analysis

Example Index

For ease of understanding, I first simulate a small portion of the data corresponding to the idx_day_aid_pvindex according to the indexing rule.

Because the leftmost column of the index idx_day_aid_pv is day, we need to iterate rows during the period from 20181220 to 20181224 to obtain the pv total from 20181220 to 20181224.

Viewing Optimizer Trace Information

# 开启 optimizer_trace
set optimizer_trace='enabled=on';
# 执行 sql 
select aid,sum(pv) as num from article_rank where day>=20181220 and day<=20181224 group by aid order by num desc limit 10;
# 查看 trace 信息
select trace from `information_schema`.`optimizer_trace`\G;

The following shows a fraction of the final execution result.

{
  "join_execution": {
    "select#": 1,
    "steps": [
      {
        "creating_tmp_table": {
          "tmp_table_info": {
            "table": "intermediate_tmp_table",
            "row_length": 20,
            "key_length": 4,
            "unique_constraint": false,
            "location": "memory (heap)",
            "row_limit_estimate": 838860
          }
        }
      },
      {
        "converting_tmp_table_to_ondisk": {
          "cause": "memory_table_size_exceeded",
          "tmp_table_info": {
            "table": "intermediate_tmp_table",
            "row_length": 20,
            "key_length": 4,
            "unique_constraint": false,
            "location": "disk (InnoDB)",
            "record_format": "fixed"
          }
        }
      },
      {
        "filesort_information": [
          {
            "direction": "desc",
            "table": "intermediate_tmp_table",
            "field": "num"
          }
        ],
        "filesort_priority_queue_optimization": {
          "limit": 10,
          "rows_estimate": 1057,
          "row_size": 36,
          "memory_available": 262144,
          "chosen": true
        },
        "filesort_execution": [
        ],
        "filesort_summary": {
          "rows": 11,
          "examined_rows": 552203,
          "number_of_tmp_files": 0,
          "sort_buffer_size": 488,
          "sort_mode": "<sort_key, additional_fields>"
        }
      }
    ]
  }
}

Analyze Fields in the Temporary Table

After the debugging with gdb, we find that the fields in the temporary table are aid and num.

Breakpoint 1, trace_tmp_table (trace=0x7eff94003088, table=0x7eff94937200) at /root/newdb/mysql-server/sql/sql_tmp_table.cc:2306
warning: Source file is more recent than executable.
2306      trace_tmp.add("row_length",table->s->reclength).
(gdb) p table->s->reclength
$1 = 20
(gdb) p table->s->fields
$2 = 2
(gdb) p (*(table->field+0))->field_name
$3 = 0x7eff94010b0c "aid"
(gdb) p (*(table->field+1))->field_name
$4 = 0x7eff94007518 "num"
(gdb) p (*(table->field+0))->row_pack_length()
$5 = 4
(gdb) p (*(table->field+1))->row_pack_length()
$6 = 15
(gdb) p (*(table->field+0))->type()
$7 = MYSQL_TYPE_LONG
(gdb) p (*(table->field+1))->type()
$8 = MYSQL_TYPE_NEWDECIMAL
(gdb)

The preceding information shows the filed types: The aid field is of type MYSQL_TYPE_LONG and occupies 4bytes; the num field is of type MYSQL_TYPE_NEWDECIMAL and occupies 15 bytes.

The SUM() and AVG() functions return a DECIMAL value for exact-value arguments (integer or DECIMAL), and a DOUBLE value for approximate-value arguments (FLOAT or DOUBLE). (Before MySQL 5.0.3, SUM() and AVG() return DOUBLE for all numeric arguments.)

According to the preceding information, the total length of the two fields is 19. However, the tmp_table_info.reclength shown in optimizer_trace is 20. Other experiments also indicate that the length of table->s->reclength is the total length of all the fields in table->field plus 1.

Process Overview

  1. Try to use the temporary table in memory on the heap to store data processed by group by and find that the memory is insufficient.
  2. Create a temporary table with two fields: aid and num (sum(pv) as num).
  3. Insert one row from the index idx_day_aid_pv into the temporary table. The insert operation follows this rule: If the aid field does not exist, insert the data row directly; if that field already exists, add the pvvalues to num.
  4. Iterate all the rows in the index idx_day_aid_pv from 20181220 to 20181224 and perform Step 3.
  5. Perform sorting by num in the temporary table in the precedence queue.
  6. Extract the 10 data rows that are finally present in the heap (the heap in the precedence queue) and directly return them as a result set without looking for them again in the table.

Here I also want to show the steps to perform precedence queue sorting:

  1. Extract 10 data rows from the temporary table (unsorted) and use the num and aid values as the 10 elements of a Min heap (that is, the smallest num value is the value of the root).
  2. Extract one row and compare its num value with the value of the heap root node. If the num value is larger than the value of the heap root node, replace it. Then perform heap sort on the new heap.
  3. Repeat Step 2 until row 552,203 is compared.

Performance Optimization

Solution 1: Use the idx_aid_day_pv index

# Query_time: 4.406927  Lock_time: 0.000200 Rows_sent: 10  Rows_examined: 1337315
SET timestamp=1552791804;
select aid,sum(pv) as num from article_rank force index(idx_aid_day_pv) where day>=20181220 and day<=20181224 group by aid order by num desc limit 10;

Why does it take 12 times less time to scan the same number of rows (1,337,315 rows)?


Example Index

For ease of understanding, I also simulate a small portion of the data corresponding to the idx_aid_day_pvindex according to the indexing rule.

A temporary table is not required for group by. One reason why this solution shows much higher performance than SQL1 is that aid values in idx_aid_day_pv are definitely ordered. Therefore, a temporary table is not required for group by. A temporary table is only required during sorting. How can we verify this? We simply need to use the two methods and compare the results.

Performance for using idx_day_aid_pv:

mysql> explain select aid,sum(pv) as num from article_rank force index(idx_day_aid_pv) where day>=20181220 and day<=20181224 group by aid order by null limit 10;
+----+-------------+--------------+------------+-------+-------------------------------+----------------+---------+------+--------+----------+-------------------------------------------+
| id | select_type | table        | partitions | type  | possible_keys                 | key            | key_len | ref  | rows   | filtered | Extra                                     |
+----+-------------+--------------+------------+-------+-------------------------------+----------------+---------+------+--------+----------+-------------------------------------------+
|  1 | SIMPLE      | article_rank | NULL       | range | idx_day_aid_pv,idx_aid_day_pv | idx_day_aid_pv | 4       | NULL | 404607 |   100.00 | Using where; Using index; Using temporary |
+----+-------------+--------------+------------+-------+-------------------------------+----------------+---------+------+--------+----------+-------------------------------------------+

Note that I use order by null to disallow the sorting of results obtained after the group by operation. If order by null is not added, the preceding SQL statement will have Using filesort

Performance for using idx_aid_day_pv:

mysql> explain select aid,sum(pv) as num from article_rank force index(idx_aid_day_pv) where day>=20181220 and day<=20181224 group by aid order by null limit 10;
+----+-------------+--------------+------------+-------+-------------------------------+----------------+---------+------+------+----------+--------------------------+
| id | select_type | table        | partitions | type  | possible_keys                 | key            | key_len | ref  | rows | filtered | Extra                    |
+----+-------------+--------------+------------+-------+-------------------------------+----------------+---------+------+------+----------+--------------------------+
|  1 | SIMPLE      | article_rank | NULL       | index | idx_day_aid_pv,idx_aid_day_pv | idx_aid_day_pv | 12      | NULL |   10 |    11.11 | Using where; Using index |
+----+-------------+--------------+------------+-------+-------------------------------+----------------+---------+------+------+----------+--------------------------+

View optimizer trace information

# 开启optimizer_trace
set optimizer_trace='enabled=on';
# 执行 sql 
select aid,sum(pv) as num from article_rank force index(idx_aid_day_pv) where day>=20181220 and day<=20181224 group by aid order by num desc limit 10;
# 查看 trace 信息
select trace from `information_schema`.`optimizer_trace`\G;

The following shows a fraction of the final execution result.

{
  "join_execution": {
    "select#": 1,
    "steps": [
      {
        "creating_tmp_table": {
          "tmp_table_info": {
            "table": "intermediate_tmp_table",
            "row_length": 20,
            "key_length": 0,
            "unique_constraint": false,
            "location": "memory (heap)",
            "row_limit_estimate": 838860
          }
        }
      },
      {
        "filesort_information": [
          {
            "direction": "desc",
            "table": "intermediate_tmp_table",
            "field": "num"
          }
        ],
        "filesort_priority_queue_optimization": {
          "limit": 10,
          "rows_estimate": 552213,
          "row_size": 24,
          "memory_available": 262144,
          "chosen": true
        },
        "filesort_execution": [
        ],
        "filesort_summary": {
          "rows": 11,
          "examined_rows": 552203,
          "number_of_tmp_files": 0,
          "sort_buffer_size": 352,
          "sort_mode": "<sort_key, rowid>"
        }
      }
    ]
  }
}

The execution process is as follows:

  1. Create a temporary table with two fields: aid and num (sum(pv) as num).
  2. Read a row in the idx_aid_day_pv and see if it matches the criterion. If the day field value is not in the range (20181220 - 20181224), read the next row; if the day filed value is in the range, accumulate the pv values (this operation is not to be performed in the temporary table).
  3. Read the next row in the idx_aid_day_pv index. If the aid value is consistent with that shown in Step 1 and meets the criterion, accumulate the pv values (this operation is not to be performed in the temporary table). If the aid value is not consistent, write the preceding result set into the temporary table.
  4. Perform Step 2 and Step 3 cyclically until all the rows of the idx_aid_day_pv index are scanned.
  5. Perform sorting by num in the temporary table in the precedence queue.
  6. Obtain the records again in the table (the temporary table) based on the first 10 rowids and return the result set.

Here I also want to show the steps to perform precedence queue sorting:

  1. Extract 10 data rows from the temporary table (unsorted) and use the num and rowid values as the 10 elements of a Min heap (that is, the smallest num value is the value of the root).
  2. Extract one row and compare its num value with the value of the heap root node. If the num value is larger than the value of the heap root node, replace it. Then perform heap sort on the new heap.
  3. Repeat Step 2 until row 552,203 is compared.

Solution Feasibility

I have found that if I add a row with the day value of 20181219, the scanning index also reads that row, although it does not meet the criterion. Because in this test all the data is within the period of the five days from 20181220 to 20181224, the scanned rows happen to be all the data rows in the table.

What if the table stores data within the period of 10 days or 365 days instead of these 5 days? Is this solution still feasible? Let's first simulate data for 10 days by extending the closing date range by another 5 days. The number of rows remains unchanged (785,102 rows).

drop procedure if exists idata;
delimiter ;;
create procedure idata()
begin
  declare i int;
  declare aid int;
  declare pv int;
  declare post_day int;
  set i=1;
  while(i<=785102)do
    set aid = round(rand()*500000);
    set pv = round(rand()*100);
    set post_day = 20181225 + i%5;
    insert into article_rank (`aid`,`pv`,`day`) values(aid, pv, post_day);
    set i=i+1;
  end while;
end;;
delimiter ;
call idata();
# Query_time: 9.151270  Lock_time: 0.000508 Rows_sent: 10  Rows_examined: 2122417
SET timestamp=1552889936;
select aid,sum(pv) as num from article_rank force index(idx_aid_day_pv) where day>=20181220 and day<=20181224 group by aid order by num desc limit 10;

The number of scanned rows is 2,122,417 because the scanning index need to iterate the entire index (the number of index rows is the number of all the table rows) and I have just inserted another 785,102 rows.

The result shows that when the data volume is doubled, the query time is also doubled. Therefore, this optimization method is not stable.

Solution 2: Increase the Maximum Space Limit of the Temporary Table

The default space of a temporary table is 16 MB.

mysql> show global variables like '%table_size';
+---------------------+----------+
| Variable_name       | Value    |
+---------------------+----------+
| max_heap_table_size | 16777216 |
| tmp_table_size      | 16777216 |
+---------------------+----------+


https://dev.mysql.com/doc/refman/5.7/en/server-system-variables.html#sysvar_tmp_table_size

max_heap_table_size: This variable sets the maximum size to which user-created MEMORY tables are permitted to grow. The value of the variable is used to calculate MEMORY table MAX_ROWS values.

Setting this variable has no effect on any existing MEMORY table, unless the table is re-created with a statement such as CREATE TABLE or altered with ALTER TABLE or TRUNCATE TABLE. A server restart also sets the maximum size of existing MEMORY tables to the global max_heap_table_size value.

tmp_table_size: The maximum size of internal in-memory temporary tables. This variable does not apply to user-created MEMORY tables. The actual limit is determined from whichever of the values of tmp_table_size   and max_heap_table_size   is smaller. If an in-memory temporary table exceeds the limit, MySQL automatically converts it to an on-disk temporary table. The internal_tmp_disk_storage_engine option defines the storage engine used for on-disk temporary tables.

That is to say, the maximum size of the temporary table is 16 MB and max_heap_table_size is also limited by tmp_table_size.

Now let's change the temporary table size to 32 MB and run the original SQL query.

set tmp_table_size=33554432;
set max_heap_table_size=33554432;
# Query_time: 5.910553  Lock_time: 0.000210 Rows_sent: 10  Rows_examined: 1337315
SET timestamp=1552803869;
select aid,sum(pv) as num from article_rank where day>=20181220 and day<=20181224 group by aid order by num desc limit 10;

Solution 3: Use SQL_BIG_RESULT

Due to the large number of query results, we specify in the optimizer that the temporary table uses the disk storage directly.

# Query_time: 6.144315  Lock_time: 0.000183 Rows_sent: 10  Rows_examined: 2122417
SET timestamp=1552802804;
select SQL_BIG_RESULT aid,sum(pv) as num from article_rank where day>=20181220 and day<=20181224 group by aid order by num desc limit 10;

The number of scanned rows is the number of all the matching rows (785,102) multiplied by 2 plus the number of all the rows after group by (552,203) and the limit value.


By the way, I also want to mention that if this solution is adopted, the query time basically remains unchanged when the data volume is doubled. The reason is that the number of scanned rows remains unchanged. In this test, the query time is 6.197484 seconds.

Conclusion

Solution 1 — does not provide a stable performance. When the data rows in the entire table are the same as the number of rows in the query range and the temporary table size limit is not reached, this solution provides the best performance. The higher the ratio of the query data to the total data volume of the entire table, the lower the optimization.

Solution 2 —requires the adjustment to the temporary table memory size and is feasible; however, when the database is larger than 32MB, this solution requires a further increase in the temporary table size.

Solution 3 —directly uses a disk to store the temporary table. Although one additional scan on the matching rows is required, the whole response time is increased by only 0.1s compared with Solution 2. In this example, the data volume is relatively small and therefore I think this time gap is acceptable.

The final conclusion is that Solution 3 is the most appropriate for this scenario.

Additional Problems

# SQL1
select aid,sum(pv) as num from article_rank where day>=20181220 and day<=20181224 group by aid order by num desc limit 10;
# SQL2
select aid,sum(pv) as num from article_rank force index(idx_aid_day_pv) where day>=20181220 and day<=20181224 group by aid order by num desc limit 10;



  1. SQL1 uses full-field sorting and does not require the retrieval of the data in the table again. Then why do we need to add 10 to obtain the total number of scanned rows?
  2. Since the number of rows after the group by operation is 552,203 for both SQL1 and SQL2, why Does SQL1 have the problem of insufficient memory? What are the details behind this problem?
  3. creating_tmp_table.tmp_table_info.row_limit_estimate in trace shows 838,860; this calculation is based on the temporary table size limit (16 MB). Because one row occupies 20 bytes, the total number of rows that can be included is 838860 (16777216/20); however, we actually need to put 785,102 rows into the temporary table. Why?
  4. Why does the number of rows to be scanned in the original table need to be multiplied by two after SQL1 uses SQL_BIG_RESULT for optimization? Why does writing into the temporary table in the memory alone lead to a 10× performance difference?
  5. From the source code, I find that the number of scanned rows shown in the trace is often not the actual number of rows. I do not understand why the actual number of scanned rows or the real capacity is not recorded in the trace, for example, the number of scanned rows in SQL1: filesort_priority_queue_optimization.rows_estimate. I see the calculation rule in gdb, as shown in the following figure.
  6. Can we use some tools to count the I/O during the execution of the SQL statement?

Do you have solutions to these problems? Are you facing similar frustrations? Comment and let me know!

Topics:
database ,database optimization ,mysql ,sql ,performance

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