MySQL 5.6 vs MySQL 5.5 & the Star Schema Benchmark
So far most of the benchmarks posted about MySQL 5.6 use the sysbench OLTP workload. I wanted to test a set of queries which, unlike sysbench, utilize joins. I also wanted an easily reproducible set of data which is more rich than the simple sysbench table. The Star Schema Benchmark (SSB) seems ideal for this.
I wasn’t going to focus on the performance of individual queries in this post, but instead intended to focus only on the overall response time for answering all of the queries in the benchmark. I got some strange results, however, which showed MySQL 5.6.10 to be much slower than MySQL 5.5.30 even with only a single connection. I felt these results warranted deeper investigation, so I did some research and detailed my findings here.
Just a few notes:
I tested two scenarios: a buffer pool much smaller than the data set (default size of 128MB, which is 1/8th of the data) and I also testing a 4G buffer pool, which is larger than the data. Very little tuning was done. The goal was to see how MySQL 5.6 performs out-of-the-box as compared to 5.5.30 with default settings. The non-default settings were tried to dig deeper into performance differences and are documented in the post.
This blog post is not a definitive conclusion about innodb_old_blocks_pct or innodb_old_blocks_time. It does highlight how a data set much larger than the buffer pool may perform worse with innodb_old_blocks_time=1000, but as I said this needs further investigation. One particular point of investigation which needs to be followed up on, including testing innodb_old_blocks_time=1000 on MySQL 5.5.30 and testing multiple buffer pools on MySQL 5.5.30. Finally, MySQL 5.6.10 has many additional tuning options which must be investigated (MRR, BKA, ICP, etc) before coming to further conclusions. These will be the topic of further blog posts.
Benchmark Details:
The SSB employs a data generator which produces data for a star schema. Star schema are commonly used for analytics because it is extremely easy to construct queries against. It is also very easy to define an OLAP cube over a star schema, so they are popular for use with tools like Mondrian and also for data mining. I wrote an earlier blog post which describes the differences between major schema types.
- I used the SSB data set at scale factor 1. Scale factor 1 results in 587MB of raw data, mostly in one table (lineorder).
- Each of the 13 queries were executed serially in a single connection
- I modified the queries to use ANSI JOIN syntax. No other changes to the queries were made.
Test Environment
- The MySQL versions used at the time of this post are 5.5.30 and 5.6.10, each of which are GA when this was written.
- I compiled both servers from source (cmake -gui .; make; make install)
- Only changes from defaults was that both servers are compiled without the PERFORMANCE_SCHEMA, and paths are unique for basedir and datadir
- I tested three configurations:
- Config 1: Default config for MySQL 5.5 and MySQL 5.6, no tuning at all
- Config 2: MySQL 5.6 with all default settings except innodb_old_blocks_time=0
- Config 3: MySQL 5.5 and 5.6 with a 4G buffer pool instead of the default 128M
Rationale:
- Since O_DIRECT is not used by default, the file system cache will give better read performance after first run (but not as good as warm buffer pool)
- Thus, the results marked COLD are the results after the server reboot, when the FS cache is cold
- The remaining results are runs without a server restart. For the default size BP, this means the FS cache is warm. For the 4G BP, the BP is completely warm.
- The idea here is to test the situation when the buffer pool is smaller than data and the IO is slow (when the FS cache is cold, IO to slow IO subsystem happens)
- Repeated runs test a buffer pool which is smaller than the data but underlying IO is fast (a warm FS cache reduces IO cost significantly)
- And finally, testing with a 4G buffer pool shows how the system performs when the data fits completely into the buffer pool (no IO on repeat runs)
Test Server:
- Intel core i970-3.20GHz. 12 logical cores (six physical cores).
- 12GB memory
- 4 disk 7200RPM RAID 10 array with 512MB write-back cache
Star Schema Benchmark – Scale Factor 1 – Mysql 5.5 vs 5.6
response times are in seconds (lower is better)
| Version | Buffer | Cold | Run1 | Run2 | Run3 |
|---|---|---|---|---|---|
| 5.5.30 | 128M | 361.49 | 189.29 | 189.34 | 189.40 |
| 5.6.10 | 128M | 362.31 | 324.25 | 320.74 | 318.84 |
| 5.6.10 (innodb_old_blocks_time=0) | 128M | 349.24 | 178.80 | 178.55 | 179.07 |
| 5.5.30 | 4G | 200.87 | 20.53 | 20.36 | 20.35 |
| 5.6.10 | 4G | 195.33 | 14.41 | 14.45 | 14.61 |
I started by running the benchmark against MySQL 5.5.30. It took 361.49 seconds to complete all 13 queries. I then repeated the run three more times. The speed is very consistent, just a few tenths of a second off per run. I then rebooted the machine and fired up 5.6.10. I ran the test, and to my surprise MySQL 5.6.10 did not get much faster during the repeat runs, compared to the initial cold run. I stopped the MySQL 5.6 server, rebooted and verified again. Same issue. This was very different from MySQL 5.5.30, which performs significantly better on the repeat warm runs.
Just to be sure it wasn’t a disk problem, I pointed the MySQL 5.6.10 at the MySQL 5.5.30 data directory. Tthe speed was essentially the same. I did some further investigation and I determined that there was a lower buffer pool hit ratio during the MySQL 5.6 runs and MySQL 5.6.10 was doing more IO as a consequence. To confirm that this was indeed the problem I decided to compare performance with a buffer pool much larger than the data size, so I configured the server with a 4GB buffer pool. I tested both versions, and as you can see above, MySQL 5.6 outperformed MySQL 5.5.30 with the big buffer pool.
Why is the MySQL 5.6.10 with default settings test significantly slower than MySQL 5.5.30 in repeat runs?
I thought about the differences in the defaults between MySQL 5.5 and MySQL 5.6 and innodb_old_blocks_time immediately came to mind. The InnoDB plugin introduced innodb_old_blocks_time to help control the behavior of the new split LRU mechanism which was implemented in the plugin. In the original InnoDB, the LRU was implemented as a classic LRU which is subject to “pollution” by full table scans. In the classic LRU, a full table scan pushes out important hot pages from the buffer pool often for an infrequent scan, like a backup or report. In an OLTP system this can have very negative performance consequences.
The plugin attempts to fix this problem by splitting the LRU into hot and cold sections. When a page is first read into the buffer pool it is first placed onto the head of the cold section of the LRU, where it begins to age of naturally. If the page is touched again while on the cold portion, it is moved to the head of the hot portion.
This sounds good in theory, but in practice it is problematic. What usually happens is that the full table scans access the table by primary key. This forces the storage engine to touch the same page numerous times in rapid succession. This invariably moves the page onto the hot area, defeating the split. In order to prevent this from happening, another variable innodb_old_blocks_time was introduced.
Innodb_old_blocks_time controls how long a page must be on the cold portion of the LRU before it is eligible to be moved to the hot portion. In MySQL 5.5 and earlier, innodb_old_blocks_time defaults to a value of 0(zero), which means that pages move rapidly from the cold portion to the hot portion because they must stay on the cold LRU for zero milliseconds before being able to move to the hot list. In MySQL 5.6 the default value of innodb_old_blocks_time is changed to 1000. The location at which a page is initially placed into the LRU is defined by innodb_old_blocks_pct. The default value on both versions is 38, which happens to be 3/8 of the buffer pool.
For this workload with a small buffer pool (the buffer pool is smaller than the working set) having innodb_old_blocks_time=1000 appears to cause a major performance regression. The new setting changes which pages end up staying in the buffer pool, and which are aged out.
Digging into why innodb_old_blocks_time change the performance?
Each “flight” of queries represents a set of drill-down queries to find an anomaly. I am going to focus on the first query, which uses only one join. Since it is practical for a query with only one join, I’ve tested performance of the query with the join in both directions.
Explain for query Q1.1:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | mysql> select straight_join sum(lo_extendedprice*lo_discount) as revenue from lineorder join dim_date on lo_orderdatekey = d_datekey where d_year = 1993 and lo_discount between 1 and 3 and lo_quantity < 25; +--------------+ | revenue | +--------------+ | 446268068091 | +--------------+ 1 row in set (33.94 sec) *************************** 1. row *************************** id: 1 select_type: SIMPLE table: lineorder type: ALL possible_keys: LO_OrderDateKey key: NULL key_len: NULL ref: NULL rows: 5996539 Extra: Using where *************************** 2. row *************************** id: 1 select_type: SIMPLE table: dim_date type: eq_ref possible_keys: PRIMARY key: PRIMARY key_len: 4 ref: ssb.lineorder.LO_OrderDateKey rows: 1 Extra: Using where 2 rows in set (0.00 sec) |
After running the query, see how many pages were read from disk versus how many page requests their were:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | mysql> select * from information_schema.global_status where variable_name like '%innodb%read%'; +---------------------------------------+----------------+ | VARIABLE_NAME | VARIABLE_VALUE | +---------------------------------------+----------------+ | INNODB_BUFFER_POOL_READ_AHEAD_RND | 0 | | INNODB_BUFFER_POOL_READ_AHEAD | 38392 | | INNODB_BUFFER_POOL_READ_AHEAD_EVICTED | 0 | | INNODB_BUFFER_POOL_READ_REQUESTS | 6731100 | | INNODB_BUFFER_POOL_READS | 570 | | INNODB_DATA_PENDING_READS | 0 | | INNODB_DATA_READ | 640536576 | | INNODB_DATA_READS | 38972 | | INNODB_PAGES_READ | 38961 | | INNODB_ROWS_READ | 6611119 | +---------------------------------------+----------------+ 10 rows in set (0.00 sec) Here are the contents of the buffer pool in pages afterwards: mysql> select sq.*, pages / (@@innodb_buffer_pool_size / 16384) * 100 pct_buffer_pool from ( select table_name, index_name, count(*) pages, sum(is_old='YES') old, count(*) - sum(is_old='YES') hot, sum(number_records) records from information_schema.innodb_buffer_page_lru where table_name like '%ssb%' group by 1,2 ) sq order by pct_buffer_pool desc; +-------------------+------------------+-------+------+------+---------+-----------------+ | table_name | index_name | pages | old | hot | records | pct_buffer_pool | +-------------------+------------------+-------+------+------+---------+-----------------+ | `ssb`.`lineorder` | GEN_CLUST_INDEX | 6909 | 2559 | 4350 | 1083172 | 84.3384 | | `ssb`.`lineorder` | LO_PartKey | 17 | 0 | 17 | 9979 | 0.2075 | | `ssb`.`lineorder` | LO_CommitDateKey | 17 | 0 | 17 | 10776 | 0.2075 | | `ssb`.`lineorder` | LO_OrderDateKey | 17 | 0 | 17 | 10376 | 0.2075 | | `ssb`.`dim_date` | PRIMARY | 17 | 0 | 17 | 2481 | 0.2075 | | `ssb`.`lineorder` | LO_CustKey | 16 | 0 | 16 | 8616 | 0.1953 | | `ssb`.`lineorder` | LO_OrderKey | 16 | 0 | 16 | 10943 | 0.1953 | | `ssb`.`lineorder` | LO_SuppKey | 15 | 0 | 15 | 11466 | 0.1831 | +-------------------+------------------+-------+------+------+---------+-----------------+ 8 rows in set (0.12 sec) And the Innodb stats: mysql> select * from information_schema.innodb_buffer_pool_statsG *************************** 1. row *************************** POOL_ID: 0 POOL_SIZE: 8191 FREE_BUFFERS: 1024 DATABASE_PAGES: 7162 OLD_DATABASE_PAGES: 2623 MODIFIED_DATABASE_PAGES: 0 PENDING_DECOMPRESS: 0 PENDING_READS: 0 PENDING_FLUSH_LRU: 0 PENDING_FLUSH_LIST: 0 PAGES_MADE_YOUNG: 3 PAGES_NOT_MADE_YOUNG: 4824154 PAGES_MADE_YOUNG_RATE: 0 PAGES_MADE_NOT_YOUNG_RATE: 0 NUMBER_PAGES_READ: 38960 NUMBER_PAGES_CREATED: 0 NUMBER_PAGES_WRITTEN: 1 PAGES_READ_RATE: 0 PAGES_CREATE_RATE: 0 PAGES_WRITTEN_RATE: 0 NUMBER_PAGES_GET: 6731253 HIT_RATE: 0 YOUNG_MAKE_PER_THOUSAND_GETS: 0 NOT_YOUNG_MAKE_PER_THOUSAND_GETS: 0 NUMBER_PAGES_READ_AHEAD: 38457 NUMBER_READ_AHEAD_EVICTED: 0 READ_AHEAD_RATE: 0 READ_AHEAD_EVICTED_RATE: 0 LRU_IO_TOTAL: 431 LRU_IO_CURRENT: 0 UNCOMPRESS_TOTAL: 0 UNCOMPRESS_CURRENT: 0 1 row in set (0.00 sec) |
Now compare the difference with innodb_old_blocks_time=0;
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | mysql> set global innodb_old_blocks_time=0; Query OK, 0 rows affected (0.00 sec) mysql> select straight_join sum(lo_extendedprice*lo_discount) as revenue from lineorder join dim_date on lo_orderdatekey = d_datekey where d_year = 1993 and lo_discount between 1 and 3 and lo_quantity < 25G *************************** 1. row *************************** revenue: 446268068091 1 row in set (7.81 sec) mysql> select * from information_schema.global_status where variable_name like '%innodb%read%'; +---------------------------------------+----------------+ | VARIABLE_NAME | VARIABLE_VALUE | +---------------------------------------+----------------+ | INNODB_BUFFER_POOL_READ_AHEAD_RND | 0 | | INNODB_BUFFER_POOL_READ_AHEAD | 38461 | | INNODB_BUFFER_POOL_READ_AHEAD_EVICTED | 0 | | INNODB_BUFFER_POOL_READ_REQUESTS | 6731687 | | INNODB_BUFFER_POOL_READS | 550 | | INNODB_DATA_PENDING_READS | 0 | | INNODB_DATA_READ | 641339392 | | INNODB_DATA_READS | 39021 | | INNODB_PAGES_READ | 39010 | | INNODB_ROWS_READ | 6611119 | +---------------------------------------+----------------+ 10 rows in set (0.00 sec) mysql> select sq.*, pages / (@@innodb_buffer_pool_size / 16384) * 100 pct_buffer_pool from ( select table_name, index_name, count(*) pages, sum(is_old='YES') old, count(*) - sum(is_old='YES') hot, sum(number_records) records from information_schema.innodb_buffer_page_lru where table_name like '%ssb%' group by 1,2 ) sq order by pct_buffer_pool desc; +-------------------+-----------------+-------+------+------+---------+-----------------+ | table_name | index_name | pages | old | hot | records | pct_buffer_pool | +-------------------+-----------------+-------+------+------+---------+-----------------+ | `ssb`.`lineorder` | GEN_CLUST_INDEX | 7085 | 2547 | 4538 | 1104291 | 86.4868 | | `ssb`.`dim_date` | PRIMARY | 17 | 17 | 0 | 2481 | 0.2075 | +-------------------+-----------------+-------+------+------+---------+-----------------+ 2 rows in set (0.11 sec) So there is more of lineorder in the buffer pool and the other secondary indexes have been pushed out of the buffer pool. mysql> select * from information_schema.innodb_buffer_pool_statsG *************************** 1. row *************************** POOL_ID: 0 POOL_SIZE: 8192 FREE_BUFFERS: 1024 DATABASE_PAGES: 7163 OLD_DATABASE_PAGES: 2624 MODIFIED_DATABASE_PAGES: 0 PENDING_DECOMPRESS: 0 PENDING_READS: 0 PENDING_FLUSH_LRU: 0 PENDING_FLUSH_LIST: 0 PAGES_MADE_YOUNG: 29501 PAGES_NOT_MADE_YOUNG: 0 PAGES_MADE_YOUNG_RATE: 951.6144640495468 PAGES_MADE_NOT_YOUNG_RATE: 0 NUMBER_PAGES_READ: 39009 NUMBER_PAGES_CREATED: 0 NUMBER_PAGES_WRITTEN: 1 PAGES_READ_RATE: 1249.8306506241734 PAGES_CREATE_RATE: 0 PAGES_WRITTEN_RATE: 0.032257023966968806 NUMBER_PAGES_GET: 6731790 HIT_RATE: 995 YOUNG_MAKE_PER_THOUSAND_GETS: 4 NOT_YOUNG_MAKE_PER_THOUSAND_GETS: 0 NUMBER_PAGES_READ_AHEAD: 38459 NUMBER_READ_AHEAD_EVICTED: 0 READ_AHEAD_RATE: 1240.5728847456533 READ_AHEAD_EVICTED_RATE: 0 LRU_IO_TOTAL: 531 LRU_IO_CURRENT: 0 UNCOMPRESS_TOTAL: 0 UNCOMPRESS_CURRENT: 0 1 row in set (0.01 sec) |
Here is the difference between innodb_buffer_pool_stats side by side:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | INNODB_OLD_BLOCKS_TIME=0 INNODB_OLD_BLOCKS_TIME=1000 *************************** 1. row ************************************************ 1. row ******* POOL_ID: 0 * POOL_ID: 0 POOL_SIZE: 8192 * POOL_SIZE: 8191 FREE_BUFFERS: 1024 * FREE_BUFFERS: 1024 DATABASE_PAGES: 7163 * DATABASE_PAGES: 7162 OLD_DATABASE_PAGES: 2624 * OLD_DATABASE_PAGES: 2623 MODIFIED_DATABASE_PAGES: 0 * MODIFIED_DATABASE_PAGES: 0 PENDING_DECOMPRESS: 0 * PENDING_DECOMPRESS: 0 PENDING_READS: 0 * PENDING_READS: 0 PENDING_FLUSH_LRU: 0 * PENDING_FLUSH_LRU: 0 PENDING_FLUSH_LIST: 0 * PENDING_FLUSH_LIST: 0 PAGES_MADE_YOUNG: 29501 * PAGES_MADE_YOUNG: 3 PAGES_NOT_MADE_YOUNG: 0 * PAGES_NOT_MADE_YOUNG: 4824154 PAGES_MADE_YOUNG_RATE: 951.6144640495468 * PAGES_MADE_YOUNG_RATE: 0 PAGES_MADE_NOT_YOUNG_RATE: 0 * PAGES_MADE_NOT_YOUNG_RATE: 0 NUMBER_PAGES_READ: 39009 * NUMBER_PAGES_READ: 38960 NUMBER_PAGES_CREATED: 0 * NUMBER_PAGES_CREATED: 0 NUMBER_PAGES_WRITTEN: 1 * NUMBER_PAGES_WRITTEN: 1 PAGES_READ_RATE: 1249.8306506241734 * PAGES_READ_RATE: 0 PAGES_CREATE_RATE: 0 * PAGES_CREATE_RATE: 0 PAGES_WRITTEN_RATE: 0.032257023966968806 * PAGES_WRITTEN_RATE: 0 NUMBER_PAGES_GET: 6731790 * NUMBER_PAGES_GET: 6731253 HIT_RATE: 995 * HIT_RATE: 0 YOUNG_MAKE_PER_THOUSAND_GETS: 4 * YOUNG_MAKE_PER_THOUSAND_GETS: 0 NOT_YOUNG_MAKE_PER_THOUSAND_GETS: 0 *NOT_YOUNG_MAKE_PER_THOUSAND_GETS: 0 NUMBER_PAGES_READ_AHEAD: 38459 * NUMBER_PAGES_READ_AHEAD: 38457 NUMBER_READ_AHEAD_EVICTED: 0 * NUMBER_READ_AHEAD_EVICTED: 0 READ_AHEAD_RATE: 1240.5728847456533 * READ_AHEAD_RATE: 0 READ_AHEAD_EVICTED_RATE: 0 * READ_AHEAD_EVICTED_RATE: 0 LRU_IO_TOTAL: 531 * LRU_IO_TOTAL: 431 LRU_IO_CURRENT: 0 * LRU_IO_CURRENT: 0 UNCOMPRESS_TOTAL: 0 * UNCOMPRESS_TOTAL: 0 UNCOMPRESS_CURRENT: 0 UNCOMPRESS_CURRENT: 0 |
As promised, here are the results from joining the tables in the other direction
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | mysql> select straight_join sum(lo_extendedprice*lo_discount) as revenue from dim_date join lineorder on lo_orderdatekey = d_datekey where d_year = 1993 and lo_discount between 1 and 3 and lo_quantity < 25G *************************** 1. row *************************** revenue: 446268068091 1 row in set (22.54 sec) mysql> explain select straight_join sum(lo_extendedprice*lo_discount) as revenue -> from dim_date join lineorder on lo_orderdatekey = d_datekey -> where d_year = 1993 and lo_discount between 1 and 3 -> and lo_quantity < 25G *************************** 1. row *************************** id: 1 select_type: SIMPLE table: dim_date type: ALL possible_keys: PRIMARY key: NULL key_len: NULL ref: NULL rows: 2704 Extra: Using where *************************** 2. row *************************** id: 1 select_type: SIMPLE table: lineorder type: ref possible_keys: LO_OrderDateKey key: LO_OrderDateKey key_len: 4 ref: ssb.dim_date.D_DateKey rows: 2837 Extra: Using where 2 rows in set (0.00 sec) mysql> select * from information_schema.global_status where variable_name like '%innodb%read%'; +---------------------------------------+----------------+ | VARIABLE_NAME | VARIABLE_VALUE | +---------------------------------------+----------------+ | INNODB_BUFFER_POOL_READ_AHEAD_RND | 0 | | INNODB_BUFFER_POOL_READ_AHEAD | 0 | | INNODB_BUFFER_POOL_READ_AHEAD_EVICTED | 0 | | INNODB_BUFFER_POOL_READ_REQUESTS | 3776369 | | INNODB_BUFFER_POOL_READS | 191571 | | INNODB_DATA_PENDING_READS | 0 | | INNODB_DATA_READ | 3140882432 | | INNODB_DATA_READS | 191581 | | INNODB_PAGES_READ | 191570 | | INNODB_ROWS_READ | 910844 | +---------------------------------------+----------------+ 10 rows in set (0.01 sec) mysql> select sq.*, pages / ((@@innodb_buffer_pool_size / 16384)) * 100 pct_buffer_pool from (select table_name, index_name, count(*) pages, sum(is_old='YES') old, count(*) - sum(is_old='YES') hot, sum(number_records) records from information_schema.innodb_buffer_page_lru where table_name like '%ssb%' group by 1,2) sq order by pct_buffer_pool desc; +-------------------+------------------+-------+------+------+---------+-----------------+ | table_name | index_name | pages | old | hot | records | pct_buffer_pool | +-------------------+------------------+-------+------+------+---------+-----------------+ | `ssb`.`lineorder` | GEN_CLUST_INDEX | 6001 | 2095 | 3906 | 964974 | 73.2544 | | `ssb`.`lineorder` | LO_OrderDateKey | 31 | 28 | 3 | 18223 | 0.3784 | | `ssb`.`dim_date` | PRIMARY | 17 | 11 | 6 | 2414 | 0.2075 | | `ssb`.`lineorder` | LO_OrderKey | 17 | 17 | 0 | 11320 | 0.2075 | | `ssb`.`lineorder` | LO_PartKey | 17 | 17 | 0 | 10095 | 0.2075 | | `ssb`.`lineorder` | LO_CustKey | 17 | 17 | 0 | 9874 | 0.2075 | | `ssb`.`lineorder` | LO_CommitDateKey | 16 | 16 | 0 | 10775 | 0.1953 | | `ssb`.`lineorder` | LO_SuppKey | 16 | 16 | 0 | 11879 | 0.1953 | +-------------------+------------------+-------+------+------+---------+-----------------+ 8 rows in set (0.11 sec) mysql> select * from information_schema.innodb_buffer_pool_statsG *************************** 1. row *************************** POOL_ID: 0 POOL_SIZE: 8192 FREE_BUFFERS: 1024 DATABASE_PAGES: 6175 OLD_DATABASE_PAGES: 2259 MODIFIED_DATABASE_PAGES: 0 PENDING_DECOMPRESS: 0 PENDING_READS: 0 PENDING_FLUSH_LRU: 0 PENDING_FLUSH_LIST: 0 PAGES_MADE_YOUNG: 62 PAGES_NOT_MADE_YOUNG: 2054952 PAGES_MADE_YOUNG_RATE: 1.0508296469551364 PAGES_MADE_NOT_YOUNG_RATE: 34829.104591447605 NUMBER_PAGES_READ: 191834 NUMBER_PAGES_CREATED: 0 NUMBER_PAGES_WRITTEN: 1 PAGES_READ_RATE: 3246.91106930391 PAGES_CREATE_RATE: 0 PAGES_WRITTEN_RATE: 0.01694886527346994 NUMBER_PAGES_GET: 3777151 HIT_RATE: 950 YOUNG_MAKE_PER_THOUSAND_GETS: 0 NOT_YOUNG_MAKE_PER_THOUSAND_GETS: 544 NUMBER_PAGES_READ_AHEAD: 0 NUMBER_READ_AHEAD_EVICTED: 0 READ_AHEAD_RATE: 0 READ_AHEAD_EVICTED_RATE: 0 LRU_IO_TOTAL: 186940 LRU_IO_CURRENT: 0 UNCOMPRESS_TOTAL: 0 UNCOMPRESS_CURRENT: 0 1 row in set (0.00 sec) |
And with innodb_old_blocks_time=0:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | mysql> select straight_join sum(lo_extendedprice*lo_discount) as revenue from dim_date join lineorder on lo_orderdatekey = d_datekey where d_year = 1993 and lo_discount between 1 and 3 and lo_quantity < 25G *************************** 1. row *************************** revenue: 446268068091 1 row in set (12.36 sec) mysql> select * from information_schema.global_status where variable_name like '%innodb%read%'; +---------------------------------------+----------------+ | VARIABLE_NAME | VARIABLE_VALUE | +---------------------------------------+----------------+ | INNODB_BUFFER_POOL_READ_AHEAD_RND | 0 | | INNODB_BUFFER_POOL_READ_AHEAD | 0 | | INNODB_BUFFER_POOL_READ_AHEAD_EVICTED | 0 | | INNODB_BUFFER_POOL_READ_REQUESTS | 3811806 | | INNODB_BUFFER_POOL_READS | 186407 | | INNODB_DATA_PENDING_READS | 0 | | INNODB_DATA_READ | 3056275456 | | INNODB_DATA_READS | 186417 | | INNODB_PAGES_READ | 186406 | | INNODB_ROWS_READ | 910844 | +---------------------------------------+----------------+ 10 rows in set (0.00 sec) mysql> select sq.*, pages / ((@@innodb_buffer_pool_size / 16384)) * 100 pct_buffer_pool from ( select table_name, index_name, count(*) pages, sum(is_old='YES') old, count(*) - sum(is_old='YES') hot, sum(number_records) records from information_schema.innodb_buffer_page_lru where table_name like '%ssb%' group by 1,2 ) sq order by pct_buffer_pool desc; +-------------------+-----------------+-------+------+------+---------+-----------------+ | table_name | index_name | pages | old | hot | records | pct_buffer_pool | +-------------------+-----------------+-------+------+------+---------+-----------------+ | `ssb`.`lineorder` | GEN_CLUST_INDEX | 6980 | 2563 | 4417 | 1119893 | 85.2051 | | `ssb`.`lineorder` | LO_OrderDateKey | 47 | 17 | 30 | 30637 | 0.5737 | | `ssb`.`dim_date` | PRIMARY | 12 | 0 | 12 | 1841 | 0.1465 | +-------------------+-----------------+-------+------+------+---------+-----------------+ 3 rows in set (0.12 sec) mysql> select * from information_schema.innodb_buffer_pool_statsG *************************** 1. row *************************** POOL_ID: 0 POOL_SIZE: 8192 FREE_BUFFERS: 1024 DATABASE_PAGES: 7047 OLD_DATABASE_PAGES: 2581 MODIFIED_DATABASE_PAGES: 0 PENDING_DECOMPRESS: 0 PENDING_READS: 0 PENDING_FLUSH_LRU: 0 PENDING_FLUSH_LIST: 0 PAGES_MADE_YOUNG: 194023 PAGES_NOT_MADE_YOUNG: 0 PAGES_MADE_YOUNG_RATE: 4850.4537386565335 PAGES_MADE_NOT_YOUNG_RATE: 0 NUMBER_PAGES_READ: 186422 NUMBER_PAGES_CREATED: 0 NUMBER_PAGES_WRITTEN: 1 PAGES_READ_RATE: 4653.858653533662 PAGES_CREATE_RATE: 0 PAGES_WRITTEN_RATE: 0.02499937501562461 NUMBER_PAGES_GET: 3811961 HIT_RATE: 952 YOUNG_MAKE_PER_THOUSAND_GETS: 50 NOT_YOUNG_MAKE_PER_THOUSAND_GETS: 0 NUMBER_PAGES_READ_AHEAD: 0 NUMBER_READ_AHEAD_EVICTED: 0 READ_AHEAD_RATE: 0 READ_AHEAD_EVICTED_RATE: 0 LRU_IO_TOTAL: 186024 LRU_IO_CURRENT: 0 UNCOMPRESS_TOTAL: 0 UNCOMPRESS_CURRENT: 0 1 row in set (0.00 sec) |
Finally, I collected SHOW PROFILES information for the faster join direction (fact -> dimension)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | select p1.seq, p1.state state, p1.duration, p2.duration, p1.cpu_user + p1.cpu_system p1_cpu, p2.cpu_user + p2.cpu_system p2_cpu, p1.context_voluntary + p1.context_involuntary p1_cs, p2.context_voluntary + p2.context_involuntary p2_cs, p1.block_ops_in + p1.block_ops_out p1_block_ops, p2.block_ops_in + p2.block_ops_out p2_block_ops, p1.page_faults_major + p1.page_faults_minor p1_pf, p2.page_faults_major + p2.page_faults_minor p2_pf from p1 join p2 using(seq) where p1.state = p2.state order by p1.duration desc; +-----+----------------------+-----------+----------+-----------+-----------+-------+-------+--------------+--------------+-------+-------+ | seq | state | duration | duration | p1_cpu | p2_cpu | p1_cs | p2_cs | p1_block_ops | p2_block_ops | p1_pf | p2_pf | +-----+----------------------+-----------+----------+-----------+-----------+-------+-------+--------------+--------------+-------+-------+ | 12 | Sending data | 33.764396 | 7.523023 | 40.173893 | 13.027019 | 4979 | 21399 | 0 | 0 | 90 | 90 | | 5 | Opening tables | 0.270664 | 0.295955 | 0.025996 | 0.024996 | 34 | 35 | 2056 | 1488 | 48 | 48 | | 2 | starting | 0.000230 | 0.000192 | 0.000000 | 0.000000 | 0 | 0 | 0 | 0 | 29 | 29 | | 9 | statistics | 0.000130 | 0.000097 | 0.000000 | 0.000000 | 0 | 0 | 0 | 0 | 26 | 26 | | 6 | init | 0.000105 | 0.000138 | 0.000000 | 0.000000 | 0 | 0 | 0 | 0 | 26 | 26 | | 10 | preparing | 0.000068 | 0.000064 | 0.000000 | 0.000000 | 0 | 0 | 0 | 0 | 14 | 14 | | 16 | freeing items | 0.000049 | 0.000117 | 0.000000 | 0.001000 | 0 | 0 | 0 | 0 | 3 | 3 | | 8 | optimizing | 0.000048 | 0.000048 | 0.000000 | 0.000000 | 0 | 0 | 0 | 0 | 8 | 8 | | 7 | System lock | 0.000031 | 0.000031 | 0.000000 | 0.000000 | 0 | 0 | 0 | 0 | 1 | 1 | | 13 | end | 0.000027 | 0.000026 | 0.000000 | 0.000000 | 0 | 0 | 0 | 0 | 1 | 1 | | 4 | checking permissions | 0.000015 | 0.000014 | 0.000000 | 0.000000 | 0 | 0 | 0 | 0 | 0 | 0 | | 15 | closing tables | 0.000015 | 0.000016 | 0.000000 | 0.000000 | 0 | 0 | 0 | 0 | 0 | 0 | | 3 | checking permissions | 0.000014 | 0.000014 | 0.000000 | 0.000000 | 0 | 0 | 0 | 0 | 0 | 0 | | 11 | executing | 0.000013 | 0.000013 | 0.000000 | 0.000000 | 0 | 0 | 0 | 0 | 0 | 0 | | 14 | query end | 0.000011 | 0.000012 | 0.000000 | 0.000000 | 0 | 0 | 0 | 0 | 0 | 0 | +-----+----------------------+-----------+----------+-----------+-----------+-------+-------+--------------+--------------+-------+-------+ 15 rows in set (0.00 sec) |
Here are my modified versions of the queries (just to use ANSI JOIN syntax):
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | -- Q1.1 select sum(lo_extendedprice*lo_discount) as revenue from lineorder join dim_date on lo_orderdatekey = d_datekey where d_year = 1993 and lo_discount between 1 and 3 and lo_quantity < 25; -- Q1.2 select sum(lo_extendedprice*lo_discount) as revenue from lineorder join dim_date on lo_orderdatekey = d_datekey where d_yearmonth = 199401 and lo_discount between 4 and 6 and lo_quantity between 26 and 35; -- Q1.3 select sum(lo_extendedprice*lo_discount) as revenue from lineorder join dim_date on lo_orderdatekey = d_datekey where d_weeknuminyear = 6 and d_year = 1994 and lo_discount between 5 and 7 and lo_quantity between 26 and 35; -- Q2.1 select sum(lo_revenue), d_year, p_brand from lineorder join dim_date on lo_orderdatekey = d_datekey join part on lo_partkey = p_partkey join supplier on lo_suppkey = s_suppkey where p_category = 'MFGR#12' and s_region = 'AMERICA' group by d_year, p_brand order by d_year, p_brand; -- Q2.2 select sum(lo_revenue), d_year, p_brand from lineorder join dim_date on lo_orderdatekey = d_datekey join part on lo_partkey = p_partkey join supplier on lo_suppkey = s_suppkey where p_brand between 'MFGR#2221' and 'MFGR#2228' and s_region = 'ASIA' group by d_year, p_brand order by d_year, p_brand; -- Q2.3 select sum(lo_revenue), d_year, p_brand from lineorder join dim_date on lo_orderdatekey = d_datekey join part on lo_partkey = p_partkey join supplier on lo_suppkey = s_suppkey where p_brand= 'MFGR#2239' and s_region = 'EUROPE' group by d_year, p_brand order by d_year, p_brand; -- Q3.1 select c_nation, s_nation, d_year, sum(lo_revenue) as revenue from customer join lineorder on lo_custkey = c_customerkey join supplier on lo_suppkey = s_suppkey join dim_date on lo_orderdatekey = d_datekey where c_region = 'ASIA' and s_region = 'ASIA' and d_year >= 1992 and d_year <= 1997 group by c_nation, s_nation, d_year order by d_year asc, revenue desc; -- Q3.2 select c_city, s_city, d_year, sum(lo_revenue) as revenue from customer join lineorder on lo_custkey = c_customerkey join supplier on lo_suppkey = s_suppkey join dim_date on lo_orderdatekey = d_datekey where c_nation = 'UNITED STATES' and s_nation = 'UNITED STATES' and d_year >= 1992 and d_year <= 1997 group by c_city, s_city, d_year order by d_year asc, revenue desc; -- Q3.3 select c_city, s_city, d_year, sum(lo_revenue) as revenue from customer join lineorder on lo_custkey = c_customerkey join supplier on lo_suppkey = s_suppkey join dim_date on lo_orderdatekey = d_datekey where (c_city='UNITED KI1' or c_city='UNITED KI5') and (s_city='UNITED KI1' or s_city='UNITED KI5') and d_year >= 1992 and d_year <= 1997 group by c_city, s_city, d_year order by d_year asc, revenue desc; -- Q3.4 select c_city, s_city, d_year, sum(lo_revenue) as revenue from customer join lineorder on lo_custkey = c_customerkey join supplier on lo_suppkey = s_suppkey join dim_date on lo_orderdatekey = d_datekey where (c_city='UNITED KI1' or c_city='UNITED KI5') and (s_city='UNITED KI1' or s_city='UNITED KI5') and d_yearmonth = 'Dec1997' group by c_city, s_city, d_year order by d_year asc, revenue desc; -- Q4.1 select d_year, c_nation, sum(lo_revenue - lo_supplycost) as profit from lineorder join dim_date on lo_orderdatekey = d_datekey join customer on lo_custkey = c_customerkey join supplier on lo_suppkey = s_suppkey join part on lo_partkey = p_partkey where c_region = 'AMERICA' and s_region = 'AMERICA' and (p_mfgr = 'MFGR#1' or p_mfgr = 'MFGR#2') group by d_year, c_nation order by d_year, c_nation; -- Q4.2 select d_year, s_nation, p_category, sum(lo_revenue - lo_supplycost) as profit from lineorder join dim_date on lo_orderdatekey = d_datekey join customer on lo_custkey = c_customerkey join supplier on lo_suppkey = s_suppkey join part on lo_partkey = p_partkey where c_region = 'AMERICA' and s_region = 'AMERICA' and (d_year = 1997 or d_year = 1998) and (p_mfgr = 'MFGR#1' or p_mfgr = 'MFGR#2') group by d_year, s_nation, p_category order by d_year, s_nation, p_category; -- Q4.3 select d_year, s_city, p_brand, sum(lo_revenue - lo_supplycost) as profit from lineorder join dim_date on lo_orderdatekey = d_datekey join customer on lo_custkey = c_customerkey join supplier on lo_suppkey = s_suppkey join part on lo_partkey = p_partkey where s_nation = 'UNITED STATES' and (d_year = 1997 or d_year = 1998) and p_category = 'MFGR#14' group by d_year, s_city, p_brand order by d_year, s_city, p_brand; |
And the schema:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | DROP TABLE IF EXISTS customer; CREATE TABLE IF NOT EXISTS customer ( C_CustomerKey int primary key, C_Name varchar(25), C_Address varchar(25), C_City varchar(10), C_Nation varchar(15), C_Region varchar(12), C_Phone varchar(15), C_MktSegment varchar(10), KEY(C_Name), KEY(C_City), KEY(C_Region), KEY(C_Phone), KEY(C_MktSegment) ); DROP TABLE IF EXISTS part; CREATE TABLE IF NOT EXISTS part ( P_PartKey int primary key, P_Name varchar(25), P_MFGR varchar(10), P_Category varchar(10), P_Brand varchar(15), P_Colour varchar(15), P_Type varchar(25), P_Size tinyint, P_Container char(10), key(P_Name), key(P_MFGR), key(P_Category), key(P_Brand) ); DROP TABLE IF EXISTS supplier; CREATE TABLE supplier ( S_SuppKey int primary key, S_Name char(25), S_Address varchar(25), S_City char(10), S_Nation char(15), S_Region char(12), S_Phone char(15), key(S_City), key(S_Name), key(S_Phone) ); DROP TABLE IF EXISTS dim_date; CREATE TABLE IF NOT EXISTS dim_date ( D_DateKey int primary key, D_Date char(18), D_DayOfWeek char(9), D_Month char(9), D_Year smallint, D_YearMonthNum int, D_YearMonth char(7), D_DayNumInWeek tinyint, D_DayNumInMonth tinyint, D_DayNumInYear smallint, D_MonthNumInYear tinyint, D_WeekNumInYear tinyint, D_SellingSeason char(12), D_LastDayInWeekFl tinyint, D_LastDayInMonthFl tinyint, D_HolidayFl tinyint, D_WeekDayFl tinyint ); DROP TABLE IF EXISTS lineorder; CREATE TABLE IF NOT EXISTS lineorder ( LO_OrderKey bigint not null, LO_LineNumber tinyint not null, LO_CustKey int not null, LO_PartKey int not null, LO_SuppKey int not null, LO_OrderDateKey int not null, LO_OrderPriority varchar(15), LO_ShipPriority char(1), LO_Quantity tinyint, LO_ExtendedPrice decimal, LO_OrdTotalPrice decimal, LO_Discount decimal, LO_Revenue decimal, LO_SupplyCost decimal, LO_Tax tinyint, LO_CommitDateKey int not null, LO_ShipMode varchar(10), KEY(LO_OrderKey, LO_LineNumber), KEY(LO_CustKey), KEY(LO_SuppKey), KEY(LO_PartKey), KEY(LO_OrderDateKey), KEY(LO_CommitDateKey) ); |
Thanks Justin, I do not see conclusion section. So you suggest innodb_old_blocks_time default value should be zero?
“This blog post is not a definitive conclusion about innodb_old_blocks_pct or innodb_old_blocks_time. It does highlight how a data set much larger than the buffer pool may perform worse with innodb_old_blocks_time=1000, but as I said this needs further investigation.”
Khan,
This setting (as many others) is workload dependent. I think the default of 1000 is better choice than 0 for many, yet I think you better to test it out to see what works best for you.
Note this variable is something what you can change online without restarting the server which makes it easier to play with in production
Khan, almost all settings have defaults that work reasonably but can be tuned for specific workloads.
Justin is using a star schema sort of workload and that’s different from OLTP. In the OLTP case we expect occasional queries that do table scans but generally it’s best to prevent them from flushing the pages used by most queries. Analytical processing workloads for which the star schema tends to be used don’t necessarily have that property and may benefit from smaller or bigger, depending on workload, innodb_old_blocks_pct and innodb_old_blocks_time = 0.
If Justin wanted to he could show settings that work better for this job and worse for pure OLTP on the same hardware. It’s routine tuning for what’s happening with a specific server. Not good or bad settings, just different.
With the previous default it was useful to show benchmarks illustrating the value of setting innodb_old_blocks_pct to a non-zero value. Now we’ve changed the default it’s useful to show the ones where 0 can be better. Just to help people to know that defaults can be tuned and when it might be a good idea.
Defaults and general recommendations are good but can’t ever replace testing on specific combinations to find out what works best.
Views are my own, for an official Oracle view consult a PR person.
James Day, MySQL Senior Principal Support Engineer, Oracle
The queries aren’t applicable to only star schema. It is likely that a reporting workload on 5.6.10 on an any type of schema could be slower than 5.5.30 if the working set is larger than the buffer pool, even with very fast io (the FS cache is the fastest IO possible). It seems to me (just my humble opinion) that MySQL should be usable for more than sysbench! So people have to know that this setting could have negative impact and test it on their workload.
You can’t just suggest that everybody upgrade to 5.6.10 and it will be all roses and faster and better! Some people from Oracle have been suggesting just that.
>If Justin wanted to he could show settings that work better for this job and worse for pure OLTP on the same hardware.
>It’s routine tuning for what’s happening with a specific server. Not good or bad settings, just different.
Actually it isn’t clear if I can, because I haven’t tested the other settings. I tested with basic out of the box settings and saw an immediate very large performance difference on the SSB. It might be that enabling new optimizer features will help, or it may not. I don’t have the data to make such predictions. So as I said, I’ll be doing more testing.
If it isn’t obvious, I had to spend a LOT of time this week figuring out why 5.6.10 was so much slower out of the box, then documenting it and taking the time to post about it. I want to test other settings and see how they work, but I can’t put that all in a single post. Thus, this post is mainly about the huge out-of-the-box difference I saw with innodb_old_blocks_time.
I did not expect my performance to drop so significantly when testing 5.6.10 and people testing it on their workloads may not be aware of all the defaults changes and which ones might result in significant (and possibly negative) performance differences. Most of the defaults changes are good for all (or nearly all) workloads, but this one seems good only for OLTP and even then, maybe not in all cases (once again, needs more testing). The only testing I’ve seen on innodb_old_blocks_time is “sysbench + mysqldump” and even then, the tests were only done when the data fits in the buffer pool. There are many workloads that are significantly different from the synthetic sysbench test and many databases have working sets larger than the buffer pool.
Note:
It would be very nice if InnoDB had multiple buffer pools and supported placing tables into named BPs, or if it supported pinning tables into the BP. But it doesn’t do those things. In either case, the dimension tables could be placed (or pinned) into a specific buffer pool and it would eliminate the problem.
In this particular case, it might be beneficial to set up an event which does an FTS on the dimension tables frequently. They are small and this will keep them in the cache.
I agree that it’s good for people to know that this setting can have a negative effect for some workloads. I think it’s a good thing that you’re writing about one of those cases and I appreciate you doing it.
James Day
Why did you choose not to use referential integrity with foreign keys?