Single Table Queries Very Slow With Indexes MariaDB - mysql

Question

I am trying to tune some slow running queries on MariaDB and have hit a brick wall. I mainly work with Postgres / Oracle and these queries times seem extremely slow to me, but I can't figure out what I'm missing.
The queries in question (see below for examples) are mostly single table queries or with one join / subquery. The table in question has around 19million rows (26.5gb) and has fairly frequent writes.
Table structure and indexes below:
CREATE TABLE products_reviews (
id bigint(20) NOT NULL AUTO_INCREMENT
,  product_id int(11) NOT NULL DEFAULT 0
,  name varchar(255) NOT NULL DEFAULT ''
,  rate tinyint(4) NOT NULL DEFAULT 0
,  created_at datetime NOT NULL
,  posted_at date NOT NULL
,  site varchar(50) NOT NULL
,  review text NOT NULL
,  details text DEFAULT NULL
,  type smallint(6) NOT NULL DEFAULT 0
,  notify smallint(6) NOT NULL DEFAULT 1
,  notify_daily int(11) NOT NULL DEFAULT 1
,  user_id int(11) DEFAULT NULL
,  updated_at datetime NOT NULL
,  admin_id int(11) DEFAULT NULL
,  status smallint(6) NOT NULL DEFAULT 0
,  comment text NOT NULL
,  notified_at datetime NOT NULL
,  reply tinyint(4) NOT NULL DEFAULT 0
,  syndicate varchar(100) NOT NULL
,  status_user tinyint(4) NOT NULL
,  reviewer_id varchar(100) NOT NULL DEFAULT ''
,  sentiment text DEFAULT NULL
,  sentiment_score float(5,2) NOT NULL DEFAULT 0.00
,  ai text DEFAULT NULL
,  active tinyint(4) NOT NULL DEFAULT 1
,  active_updated_at datetime NOT NULL DEFAULT current_timestamp()
,  review_translate text NOT NULL
,  price float(6,2) NOT NULL
,  tagging text NOT NULL
,  site_id int(11) NOT NULL
,  hash varchar(255) NOT NULL
,  PRIMARY KEY (id)
,  KEY created_at (created_at)
,  KEY posted_at (posted_at)
,  KEY site (site)
,  KEY product_id (product_id)
,  KEY admin_id (admin_id)
,  KEY type (type)
,  KEY notify (notify)
,  KEY active (active)
,  KEY admin_posted (admin_id,posted_at)
,  KEY reviewer_id (reviewer_id)
,  KEY idx_products_reviews_pspth (product_id,site,posted_at,type,hash)
,  KEY idx_products_reviews_pa (product_id,active)
,  KEY idx_products_reviews_product_posted (product_id,posted_at)
,  KEY idx_products_reviews_product_adminid_postedid (product_id,admin_id,posted_at)
,  KEY product_id_2 (product_id,site,active)
) ENGINE=InnoDB AUTO_INCREMENT=27316062 DEFAULT CHARSET=utf8
Mariadb settings below:
alter_algorithm DEFAULT
aria_block_size 8192
aria_checkpoint_interval 30
aria_checkpoint_log_activity 1048576
aria_encrypt_tables OFF
aria_force_start_after_recovery_failures 0
aria_group_commit none
aria_group_commit_interval 0
aria_log_file_size 1073741824
aria_log_purge_type immediate
aria_max_sort_file_size 9223372036853727232
aria_page_checksum ON
aria_pagecache_age_threshold 300
aria_pagecache_buffer_size 134217728
aria_pagecache_division_limit 100
aria_pagecache_file_hash_size 512
aria_recover_options BACKUP,QUICK
aria_repair_threads 1
aria_sort_buffer_size 268434432
aria_stats_method nulls_unequal
aria_sync_log_dir NEWFILE
aria_used_for_temp_tables ON
auto_increment_increment 1
auto_increment_offset 1
autocommit ON
automatic_sp_privileges ON
back_log 100
basedir /usr
big_tables OFF
binlog_annotate_row_events ON
binlog_cache_size 32768
binlog_checksum CRC32
binlog_commit_wait_count 0
binlog_commit_wait_usec 100000
binlog_direct_non_transactional_updates OFF
binlog_file_cache_size 16384
binlog_format MIXED
binlog_optimize_thread_scheduling ON
binlog_row_image FULL
binlog_stmt_cache_size 32768
bulk_insert_buffer_size 8388608
check_constraint_checks ON
column_compression_threshold 100
column_compression_zlib_level 6
column_compression_zlib_strategy DEFAULT_STRATEGY
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completion_type NO_CHAIN
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connect_timeout 10
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date_format %Y-%m-%d
datetime_format %Y-%m-%d %H:%i:%s
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default_regex_flags
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default_tmp_storage_engine MyISAM
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optimizer_switch index_merge=off,index_merge_union=on,index_merge_sort_union=on,index_merge_intersection=on,index_merge_sort_intersection=off,engine_condition_pushdown=off,index_condition_pushdown=on,derived_merge=on,derived_with_keys=on,firstmatch=on,loosescan=on,materialization=on,in_to_exists=on,semijoin=on,partial_match_rowid_merge=on,partial_match_table_scan=on,subquery_cache=on,mrr=off,mrr_cost_based=off,mrr_sort_keys=off,outer_join_with_cache=on,semijoin_with_cache=on,join_cache_incremental=on,join_cache_hashed=on,join_cache_bka=on,optimize_join_buffer_size=off,table_elimination=on,extended_keys=on,exists_to_in=on,orderby_uses_equalities=on,condition_pushdown_for_derived=on,split_materialized=on
optimizer_use_condition_selectivity 1
performance_schema OFF
performance_schema_accounts_size -1
performance_schema_digests_size -1
performance_schema_events_stages_history_long_size -1
performance_schema_events_stages_history_size -1
performance_schema_events_statements_history_long_size -1
performance_schema_events_statements_history_size -1
performance_schema_events_waits_history_long_size -1
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performance_schema_users_size -1
pid_file /var/run/mariadb/mariadb.pid
plugin_dir /usr/lib64/mysql/plugin/
plugin_maturity gamma
port 3306
preload_buffer_size 32768
profiling OFF
profiling_history_size 15
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protocol_version 10
proxy_protocol_networks
proxy_user
pseudo_slave_mode OFF
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rand_seed1 93838501
rand_seed2 36662033
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replicate_annotate_row_events ON
replicate_do_db
replicate_do_table
replicate_events_marked_for_skip REPLICATE
replicate_ignore_db
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report_password
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report_user
rowid_merge_buff_size 8388608
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session_track_system_variables autocommit,character_set_client,character_set_connection,character_set_results,time_zone
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slave_ddl_exec_mode IDEMPOTENT
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slave_parallel_mode conservative
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slave_transaction_retry_errors 1213,1205
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socket /san/svc-fs/mysql/mysql.sock
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ssl_ca
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timestamp 1644789513.087889
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tmpdir /san/svc-fs/mysqltmp
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version 10.3.21-MariaDB-log
version_comment MariaDB Server
version_compile_machine x86_64
version_compile_os Linux
version_malloc_library jemalloc 3.6.0-0-g46c0af68bd248b04df75e4f92d5fb804c3d75340
version_source_revision e5e5877740f248de848219ee3a1d2881cd5c5b82
version_ssl_library OpenSSL 1.0.2k-fips  26 Jan 2017
wait_timeout 600
warning_count 0
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wsrep_cluster_address
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wsrep_data_home_dir /san/svc-fs/mysql/
wsrep_dbug_option
wsrep_debug OFF
wsrep_desync OFF
wsrep_dirty_reads OFF
wsrep_drupal_282555_workaround OFF
wsrep_forced_binlog_format NONE
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wsrep_max_ws_rows 0
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wsrep_node_address
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wsrep_notify_cmd
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wsrep_patch_version wsrep_25.24
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wsrep_restart_slave OFF
wsrep_retry_autocommit 1
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wsrep_slave_uk_checks OFF
wsrep_slave_threads 1
wsrep_sst_auth
wsrep_sst_donor
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wsrep_sst_method rsync
wsrep_sst_receive_address AUTO
wsrep_start_position 00000000-0000-0000-0000-000000000000:-1
wsrep_sync_wait 0
The queries filters can be selected on the front end by a user so the where clause can vary a bit. They will all contain a product_id at least. The speed to read from disk seems absurdly slow for the number of rows being fetched.
Example queries:
This one takes 400seconds to return
Edit - Below query is in an incorrect format which must have happened when creating the post. Original is below
SELECT SQL_NO_CACHE
pr.rate as star, pr.product_id, pr.site, COUNT(pr.id) AS count
FROM products_reviews pr
JOIN products p ON p.id = pr.product_id
AND pr.active > 0
AND pr.posted_at >= '2020-10-26'
AND pr.posted_at <= '2022-01-24 23:59'
AND pr.admin_id = 3598
GROUP BY pr.rate, pr.product_id, pr.site;
SELECT rate as star, product_id, site, COUNT(id) AS count
FROM products_reviews
WHERE product_id IN (SELECT id FROM products WHERE admin_id = 3598)
AND active >0 AND posted_at >= '2020-10-26'
AND posted_at <= '2022-01-24 23:59'
AND admin_id = 3598
GROUP BY rate, product_id, site
Explain plan
{
"query_block": {
"select_id": 1,
"filesort": {
"sort_key": "products_reviews.rate, products_reviews.product_id, products_reviews.site",
"temporary_table": {
"table": {
"table_name": "products_reviews",
"access_type": "range",
"possible_keys": [
"posted_at",
"product_id",
"admin_id",
"active",
"admin_posted",
"idx_products_reviews_pspth",
"idx_products_reviews_pa",
"idx_products_reviews_product_posted",
"idx_products_reviews_product_adminid_postedid",
"product_id_2",
"idx_products_aaprps_covering",
"idx_products_aprps_covering_t"
],
"key": "idx_products_aaprps_covering",
"key_length": "6",
"used_key_parts": ["admin_id", "active"],
"rows": 1826162,
"filtered": 100,
"attached_condition": "products_reviews.admin_id = 3598 and products_reviews.active > 0 and products_reviews.posted_at >= '2020-10-26' and products_reviews.posted_at <= '2022-01-24 23:59'",
"using_index": true
},
"table": {
"table_name": "products",
"access_type": "eq_ref",
"possible_keys": ["PRIMARY", "id", "admin_id"],
"key": "PRIMARY",
"key_length": "8",
"used_key_parts": ["id"],
"ref": ["metabase.products_reviews.product_id"],
"rows": 1,
"filtered": 100,
"attached_condition": "products.admin_id = 3598 and products_reviews.product_id = products.`id`"
}
}
}
}
}
I ran profiling on the transaction and the whole time is spent on "Sending Data".
Here is a second query, this one takes around 32 seconds.
SELECT site, MIN(posted_at) as posted_at, AVG(IF(rate>0, rate, NULL)) as rate, COUNT(IF(type=0, 1, NULL)) as count, COUNT(IF(type=0 AND syndicate='', 1, NULL)) as count_ns, AVG(IF(rate>0 AND syndicate='', rate, NULL)) as rate_ns, COUNT(IF(type=1, 1, NULL)) as count_qe
FROM products_reviews
WHERE product_id = '24828'
AND active >0
GROUP BY site
Explain Plan
{
"query_block": {
"select_id": 1,
"table": {
"table_name": "products_reviews",
"access_type": "ref",
"possible_keys": [
"product_id",
"active",
"idx_products_reviews_pspth",
"idx_products_reviews_pa",
"idx_products_reviews_product_posted",
"idx_products_reviews_product_adminid_postedid",
"product_id_2"
],
"key": "product_id_2",
"key_length": "4",
"used_key_parts": ["product_id"],
"ref": ["const"],
"rows": 6575,
"filtered": 100,
"attached_condition": "products_reviews.product_id <=> '24828' and products_reviews.active > 0"
}
}
{
"query_block": {
"select_id": 1,
"table": {
"table_name": "products_reviews",
"access_type": "ref",
"possible_keys": [
"product_id",
"active",
"idx_products_reviews_pspth",
"idx_products_reviews_pa",
"idx_products_reviews_product_posted",
"idx_products_reviews_product_adminid_postedid",
"product_id_2"
],
"key": "product_id_2",
"key_length": "4",
"used_key_parts": ["product_id"],
"ref": ["const"],
"rows": 6575,
"filtered": 100,
"attached_condition": "products_reviews.product_id <=> '24828' and products_reviews.active > 0"
}
}
}
Possibly partially related to cardinality. Here's a count of rows, distinct product_id and distinct posted_at.
count(*) 19128090
count(distinct(product_id)) 28821
count(distinct(posted_at)) 7551
The server has 96gb RAM and a 480GB SATA SSD. The buffer pool settings look well optimized to me. I tried increasing innodb_io_capacity but that had a negative impact.
If there is nothing I can do to speed up the queries with regards query tuning or parameter tuning, I wondered about keeping the table in memory at all times. They seem to get flushed at least once a day (probably around the time backups are taken). Would it be sensible to have a job that runs once every couple of hours to select * from the table?
Many thanks.

Answer 1

It's a good question you ask. My answer has a few dimensions.
Server provisioning: Your machine is adequate, and possibly overprovisioned. You have allocated 2/3 of your server's RAM to your 64GiB InnoDB buffer pool (innodb_buffer_pool_size) and that's probably enough to hold your tables. You could allocate another 8 - 12 GiB to it and still have RAM left over for your server's other functions. But buffer pool size is almost certainly not your issue.
Many indexes: You have quite a few single-column indexes. Are you sure they are all needed? Generally speaking, we create indexes to support particular query patterns, not because they might be helpful someday. If you can drop some of your indexes, do so. And, indexes slow down INSERTs and UPDATEs because both the table data and indexes are affected. Which brings us to ...
Frequent updates: It's possible that your update operations slow down the reporting-style queries you showed us in your question because the server is struggling to give consistent results (using locks) as updates happen. Try giving this SQL statement right before your too-slow query.
SET TRANSACTION ISOLATION LEVEL READ UNCOMMITTED;
This causes a "dirty read". It cuts down on locking, but gives you the chance you'll get an older version of rows that are being actively updated. That's usually no problem for reporting-style queries. Try it and see how much it helps.
Equality and range filters, covering BTREE indexes. Consider your first query, repeated here:
SELECT SQL_NO_CACHE
pr.rate as star, pr.product_id, pr.site, COUNT(pr.id) AS count
FROM products_reviews pr
JOIN products p ON p.id = pr.product_id
AND pr.active > 0
AND pr.posted_at >= '2020-10-26'
AND pr.posted_at <= '2022-01-24 23:59'
AND pr.admin_id = 3598
GROUP BY pr.rate, pr.product_id, pr.site;
You filter product_reviews on admin_id for equality, then active and posted at for ranges, then you look up rows in products, then group by rate, product_id, and site.
Try using this so-called covering index.
CREATE INDEX rate_product_site_rollup ON products_review
(admin_id, posted_at, active, rate, product_id, site);
It contains the data your query needs from the products_review table. To satisfy the query, the database can random-access the index to the first eligible row (admin_id = nnnn and the first eligible posted_at row. It can then scan the index sequentially to filter out active <= 0 rows. Finally, your GROUP BY columns are there already sorted in an order suitable for quick grouping.
You might consider a similar covering index for your second query.
(Some makes and versions of database servers allow INCLUDE(col, col, col) clauses in index declarations, but not MariaDB / MySQL. INCLUDE is very useful for covering indexes, but including the columns in the index works too.)
More than one range filter: BTREE indexes do their fastest work when you only have one range filter in your query. If your business rules allowed you to filter on pr.active = 1 then this index would satisfy your query even faster.
CREATE INDEX rate_product_site_rollup ON products_review
(admin_id, active, posted_at, rate, product_id, site);
Extraneous JOIN: You JOIN to your products table, but don't use any columns from it. So the JOIN serves only one purpose: to exclude reviews that have no matching product. Is this step necessary?
Note: pr.posted_at < '2022-01-24' + INTERVAL 1 DAY, with <, is a bit more reliable on the almost-midnight edge case than pr.posted_at <= '2022-01-24 23:59'

Answer 2

`products is only sort of used. If you don't really need it, get rid of it. I see two things that can matter:
If there is no matching row from products the JOIN will avoid including it.
If there are multiple rows in products, then the COUNT will increase.
Replace
JOIN products p ON p.id = pr.product_id
AND
with
WHERE
As a rule:
Use ON to specify how the tables are related.
Use WHERE to specify which rows to keep.
Another INDEX tip: When you need INDEX(a,b), you can do without INDEX(a). I am thinking of (admin_id, posted_at)
If active is only "0" or "1", then use AND active = 1, plus change to INDEX(admin_id, active, posted_at)
"indexes could only contain one equality operator" -- The rule is more like "Indexes should contain only one range operator, and it needs to be last". (Note: "covering" is a deliberate exception.)
(I agree that the 'covering' index may help, but I generally avoid indexes with lots of columns.)
I have the master of that MariaDB link (that RLOG mentioned); it has been updated a few times here: http://mysql.rjweb.org/doc.php/index_cookbook_mysql