Recently Mark Callaghan blogged about using O_DIRECT for the InnoDB transaction log. He noted that there was not a significant performance gain if the number of concurrent connections increases. I've done my share of testing and retesting over the past months to determine how useful this is. Based on a small TPCC workload (100 warehouses, 64 connections, 1 hour test, 5 minute rampup), I've seen huge performance gains by setting ALL_O_DIRECT for the variable innodb_flush_method using Percona XtraDB.
Without using Direct I/O, the benchmark generated a TpmC score of approximately 24,500 (HP DL160 G6, 2 x Xeon E5620 2.40GHz, 16GB mem, 4x300 GB SAS, RAID-10). After setting the variable to ALL_O_DIRECT, TpmC score went up to 48,000. Huge increase. This deserves some more investigation and some more testing. I want to also try this out on some older hardware to see if similar performance gains can be achieved.
Tuesday, June 28, 2011
Friday, May 20, 2011
Effect of changing InnoDB Page Size
Was bored so I started playing with what the performance impact of changing the innodb page size was. Interesting results from XtraDB (which makes it oh so easy to change the page size). I won't go into the gory details but the testing performed was against a machine with 6x146GB SAS drives and a read-only sysbench test (50 million records, 10GB innodb buffer pool):
The graph shows that the good old defaults work quite nicely. I'll have to get my hands on some machines with SSD drives and try this again :)
The graph shows that the good old defaults work quite nicely. I'll have to get my hands on some machines with SSD drives and try this again :)
Saturday, April 23, 2011
Covering indexes in MySQL - revisited (with benchmark)
In the process of building a new benchmark tool for Yahoo, I needed a good "guinea pig." I think I found the one by showing how much more powerful covering indexes can be with InnoDB. A covering index is one where the index itself contains all of the necessary data field(s). In other words, no need to access the data page!
Here's a sample table:
It's a wide table with 88 some odd columns with an average row length of 240 bytes.
Now to test the QPS difference between a query like this:
and a query that uses a covering index:
I have an SQL file that contains 2000 SQL statements with fairly random ids, and I'll run my benchmark program with 4 clients a few times to get a good QPS reading (throwing out the low & high numbers).
Results with SELECT *:
Results with covering index:
Fairly significant results. I've seen many times where SQL has been hastily written just to perform a simple check if a record exists or not. I've always tried to rationalize this with the developers I work with, but I never had the raw numbers to show the exact impact. If the query happens only a handful of times, not a huge deal. But, if it happens hundreds of thousands or millions of times per day, then that's a different story, and making a very simple rewrite of the SQL helps immensely, and could even save on cost, if you're thinking about adding another slave to spread out load.
Here's a sample table:
CREATE TABLE entity ( `id` int(10) unsigned NOT NULL AUTO_INCREMENT, `entity_type` enum('Advertiser','Publisher','Network') NOT NULL DEFAULT 'Advertiser', `managing_entity_id` int(10) unsigned DEFAULT NULL, .... PRIMARY KEY (`id`), KEY `ix_entity_managing_entity_id` (`managing_entity_id`), .... ) ENGINE=InnoDB DEFAULT CHARSET=utf8;
It's a wide table with 88 some odd columns with an average row length of 240 bytes.
Now to test the QPS difference between a query like this:
SELECT * FROM entity WHERE entity.managing_entity_id = X;
and a query that uses a covering index:
SELECT entity.managing_entity_id FROM entity WHERE entity.managing_entity_id = X;
I have an SQL file that contains 2000 SQL statements with fairly random ids, and I'll run my benchmark program with 4 clients a few times to get a good QPS reading (throwing out the low & high numbers).
Results with SELECT *:
Tot Q=2000,Tot T=1.757571,QPS=1137.93411475269,Min=0.0006665,Max=0.027022,90th Pctl=0.000946 Tot Q=2000,Tot T=1.770522,QPS=1129.61036349732,Min=0.0006635,Max=0.026858,90th Pctl=0.00096675 Tot Q=2000,Tot T=1.753147,QPS=1140.80564835693,Min=0.00066325,Max=0.026966,90th Pctl=0.00095175
Results with covering index:
Tot Q=2000,Tot T=0.703581,QPS=2842.60092299252,Min=0.00026625,Max=0.014821,90th Pctl=0.00035325 Tot Q=2000,Tot T=0.897792,QPS=2227.6874821785,Min=0.00026625,Max=0.04546425,90th Pctl=0.0003595 Tot Q=2000,Tot T=0.720616,QPS=2775.403266094,Min=0.000257,Max=0.01566475,90th Pctl=0.0003452
Fairly significant results. I've seen many times where SQL has been hastily written just to perform a simple check if a record exists or not. I've always tried to rationalize this with the developers I work with, but I never had the raw numbers to show the exact impact. If the query happens only a handful of times, not a huge deal. But, if it happens hundreds of thousands or millions of times per day, then that's a different story, and making a very simple rewrite of the SQL helps immensely, and could even save on cost, if you're thinking about adding another slave to spread out load.
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