Process 3 Million rows data for every 5 minutes.
Data flow: LoadData from network to tmp table.
Do some kind of group up and save to ~10 different table.
Max 7 session running for saving data to different table at the same time.
What I tired.
- Create 100GB memory disk with /etc/fstab. [tmpfs /mnt/ramDisk tmpfs size=100G 0 0] and link to my database dir. Makes 30% faster.
- Create 50GB memory disk with '/etc/fstab', using for tmpdir.that makes db 15% faster.
- Each table may have 100M rows, Insert 1M rows for every 5min.
I tried Innodb. seems one insert never ends. so I use MYISAM for All table. normally finish in 30 seconds. - For Mysql memory engine is fixed length, I have some varchar(256) column, so I use MYISAM table in memory disk. use only 1/4 memory space compare to memory engine.
- Mysql config
datadir=/data/MySQL
tmpdir = /mnt/sqlTmp
socket=/var/lib/mysql/mysql.sock
symbolic-links=0
log-error=/var/log/mysqld.log
pid-file=/var/run/mysqld/mysqld.pid
tmp_table_size=20G
max_heap_table_size=20G
max_connections = 20
key_buffer_size = 1G
sort_buffer_size = 2M
sql-mode = "NO_ZERO_IN_DATE"
secure-file-priv=""
innodb_buffer_pool_size = 1M
innodb_max_undo_log_size = 1M
innodb_log_file_size = 1M
innodb_flush_log_at_trx_commit = 0
character-set-client-handshake = FALSE
character-set-server = latin1
collation-server = latin1_bin
- CPU:E5-2620 2.1GHz * 32 Core RAM:164GB Disk:Raid10 10TB Mysql 5.7.26
- Show Processlist
- User sleep
SET @null = SLEEP(2)
- Opening tables
SET @ret = sys_exec(...
- Creating sort index
INSERT t_sav_day_hst(...
- Creating sort index
INSERT t_sav_day_uri(...
- User sleep
SET @null = SLEEP(2)
- executing
LOAD DATA INFILE ...
- User sleep
- There are still space in Memory, not even used as cache. Checked with htop.
- I put everything in memory,so there is no Disk I/O traffic. I checked with glances.
- There won't be table lock during process, I schedule it well.
- Most sql is insert on duplicate 1M data to table which has 40M row.
My question. I run max 7 sql query at same time. but if I run less query at same time. every query runs faster. what is the bottleneck. how could I make it run faster? ( All table in memory, mysql tmpdir in memory, no table lock)
Some more info.
Data flow:
1. Load Data from formatted text file to 'Temp' table. as ETL process.
2. Group by temp table and save to different 'Staging' table with IODKU.
3. Update Id to temp table from staging table.Due to I record count. so I use IODKU instead of Insert ignore.
Staging table.
CREATE TABLE `t_sav_day_uri` (
`_utDay` smallint(5) unsigned NOT NULL,
`_uid` int(10) unsigned NOT NULL AUTO_INCREMENT,
`_md5` binary(16) NOT NULL, # MD5 of a URI
`_uriTc` varchar(255) NOT NULL,
`_uriTail` varchar(512) NOT NULL,
`_did` int(10) unsigned NOT NULL,
`_hid` int(10) unsigned NOT NULL,
`_attrUri` tinyint(3) unsigned NOT NULL,
`_tfcTot` mediumint(8) unsigned NOT NULL DEFAULT '0',
`_tfcSlfCln` mediumint(8) unsigned NOT NULL DEFAULT '0',
`_tfcOthCln` mediumint(8) unsigned NOT NULL DEFAULT '0',
`_tfcEmlCln` mediumint(8) unsigned NOT NULL DEFAULT '0',
`_tfcHtpCln` mediumint(8) unsigned NOT NULL DEFAULT '0',
`_tfcHtpSpm` mediumint(8) unsigned NOT NULL DEFAULT '0',
`_tfcMltCln` mediumint(8) unsigned NOT NULL DEFAULT '0',
`_tfcMltSpm` mediumint(8) unsigned NOT NULL DEFAULT '0',
`_seeIdyFst` smallint(3) unsigned NOT NULL DEFAULT '0',
`_seeIdyLst` smallint(3) unsigned NOT NULL DEFAULT '0',
`_seeSlt` smallint(5) unsigned NOT NULL DEFAULT '0',
`_evl` tinyint(3) NOT NULL DEFAULT '0',
PRIMARY KEY (`_utDay`,`_uid`),
UNIQUE KEY `iu_sdu_md5` (`_utDay`,`_md5`) USING BTREE,
KEY `ix_sdu_hid` (`_utDay`,`_hid`) USING BTREE,
KEY `ix_sdu_uid` (`_utDay`,`_did`) USING BTREE
) ENGINE=MyISAM DEFAULT CHARSET=latin1
/*!50100 PARTITION BY RANGE (_utDay) # Divide by day.
(PARTITION p18015 VALUES LESS THAN (18016) ENGINE = MyISAM),
(PARTITION p18016 VALUES LESS THAN (18017) ENGINE = MyISAM) */
Insert to Staging table from temp table.
INSERT t_sav_day_uri(_utDay, _md5, _uriTc, _uriTail, _did, _hid, _attrUri, _tfcTot, _tfcSlfCln, _tfcOthCln, _tfcEmlCln
, _tfcHtpCln, _tfcHtpSpm, _tfcMltCln, _tfcMltSpm, _seeSlt, _seeIdyFst, _seeIdyLst)
SELECT utDay_,_md5, LEFT(ANY_VALUE(_uriTc), 255), LEFT(ANY_VALUE(_uriTail), 255)
, ANY_VALUE(_did), ANY_VALUE(_hid), ANY_VALUE(_attrUri) AS _attrUri
, SUM(1) AS _tfcTot, SUM(_attrSig & 1 = 0 ) AS _tfcSlfCln, SUM(_attrSig & 32 = 0 ) AS _tfcOthCln, SUM(_attrSig & 33 = 0 ) AS _tfcEmlCln
, SUM(_attrUri & 8+16 > 0 AND _attrSig & 1 = 0) AS _tfcHtpCln , SUM(_attrUri & 8+16 > 0 AND _attrSig & 1 > 0) AS _tfcHtpSpm # Http(s)
, SUM(_attrUri & 32 > 0 AND _attrSig & 1 = 0) AS _tfcMltCln , SUM(_attrUri & 32 > 0 AND _attrSig & 1 > 0) AS _tfcMltSpm # MailTo
, 1, utIdy_, utIdy_
FROM t_ipt3_uri
GROUP BY _md5
ON DUPLICATE KEY UPDATE
t_sav_day_uri._tfcTot = t_sav_day_uri._tfcTot + VALUES(_tfcTot) , t_sav_day_uri._tfcSlfCln = t_sav_day_uri._tfcSlfCln + VALUES(_tfcSlfCln)
, t_sav_day_uri._tfcOthCln = t_sav_day_uri._tfcOthCln + VALUES(_tfcOthCln), t_sav_day_uri._tfcEmlCln = t_sav_day_uri._tfcEmlCln + VALUES(_tfcEmlCln)
, t_sav_day_uri._tfcHtpCln = t_sav_day_uri._tfcHtpCln + VALUES(_tfcHtpCln), t_sav_day_uri._tfcHtpSpm = t_sav_day_uri._tfcHtpSpm + VALUES(_tfcHtpSpm)
, t_sav_day_uri._tfcMltCln = t_sav_day_uri._tfcMltCln + VALUES(_tfcMltCln), t_sav_day_uri._tfcMltSpm = t_sav_day_uri._tfcMltSpm + VALUES(_tfcMltSpm)
, t_sav_day_uri._seeIdyFst = IF(t_sav_day_uri._seeIdyFst = 0, VALUES(_seeIdyFst), t_sav_day_uri._seeIdyFst)
, t_sav_day_uri._seeSlt = t_sav_day_uri._seeSlt + VALUES(_seeSlt)
, t_sav_day_uri._seeIdyLst = VALUES(_seeIdyLst);
Update ID to temp table with Staging table.
UPDATE t_ipt3_uri AS A INNER JOIN t_sav_day_uri AS B ON B._utDay = utDay_ AND B._md5 = A._md5 SET A._uid = B._uid;
Show global variables
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Best Answer
8.0 no longer converts
VARCHAR
toCHAR
Have you shrunken all the datatypes as much as practical?
Have you gotten rid of redundant/unnecessary indexes?
What steps are happening in the ETL process?
Is the ingestion single-threaded or multi-threaded? This has some tips on such.
That seems strange for an
INSERT
; please provide more info.Let's see
SHOW GLOBAL STATUS;
andSHOW VARIABLES;
-- there may be some clues there.You say a table has 100M rows and is loaded 1M at a time? What happens when gets 'full'? Do you purge old data? Or what?
Your innodb settings are unworkably tiny.
How many different MEMORY tables do you need at once? You have a lot of RAM, but the 20G limit may eat it up fast.
Is the IODKU running one row at a time? Or multiple rows? Or from a SELECT?
Big transactions in InnoDB
If the
SELECT
generates a million rows, then there are probably a large number of inserts and of updates. I know that a million updates (or even 100K updates) in a single transaction is rather slow. This is because of hanging onto the old row in order to recover just in case there is a crash. This is essentially unavoidable in InnoDB, but there may be a workaround...If you do the IODKU 1K (or maybe 10K) rows at a time, each transaction is more manageable, and might run faster in spite of the overhead of looping. This discusses chunking of deletes, but most of it applies to updates.
Another tip on a big IODKU (chunked or not) is to sort the incoming data (via
ORDER BY
on theSELECT
) so that the rows are fed to IODKU inPRIMARY KEY
orUNIQUE
order.Oops, your table is MyISAM...
Big IODKU in MyISAM
The chunking (above) is probably not useful.
Table
t_sav_day_uri
will be locked for the entire million rows.I see that you are using the MyISAM feature with the AI column second in the PK.
I assume
_utDay
has the same value for the entire million rows?The
UNIQUE
key becomes the critical one. Because of theMD5()
the tests and inserts into that index will be very random. I compute that each entry in that index will be 27 bytes, leading to an average of 26 rows per 1K index block (after allowing for BTree fragmentation). Those blocks are cached in the key_buffer, which has size 1GB. The first million index entries will take about 40MB in the key_buffer. But, after about 25M rows, thrashing will begin. It will be slight at first, but grow in intensity. Changingkey_buffer_size
to 4G would solve that. But there is still more. There are 3 other indexes that need caching in the key_buffer. The PK (with an AI) could survive with room for only one leaf node. I don't know the patterns of the other indexes. On the safe side,(and adjust some other memory usage as needed)
Or, if you sort the
SELECT
, you could drop back to perhaps 8G. However the cost of theORDER BY
is a tradeoff to weigh.What does
SHOW TABLE STATUS
show for that table?Meanwhile, the data will either be thrashed or cached. If most of the IODKUs are INSERTs, then the caching of the data is not a big deal.
But if it is mostly updates,... A row might be 200 bytes. 100M rows would be 20GB. Every row to update would have to be read-modified-written. This happens in the OS, using its block size (4KB?). So, if there is upwards of 20GB (15 is probably sufficient) left over after MySQL, the OS, and anything else, then the data will be as efficient as is practical.
It gets messy if the length of a MyISAM row changes due to an update. I don't want to attempt analyzing it.