I had the opportunity to test the Memory engine during the summer. What I expected to be a very simple exercise in running the Sysbench benchmark turned out to be a lot more difficult than I expected.
My first experiments with the Memory engine gave very dismaying results. I got 0-2 TPS which is ridiculously bad. So I couldn't really think this was proper results, so I started searching for problems in my benchmarking environment. Eventually I started setting up a normal MySQL client session to the MySQL Server while the benchmark was running and issued some of the queries in the benchmark by hand and I was surprised to see some simple queries take seconds.
EXPLAIN came to my rescue. EXPLAIN showed that the range scans in Sysbench was in fact turned into full table scans. Now this was surprising given that the primary key index in most engines is always ordered, so a range scan should normally be translated to a simple ordered index scan.
What I found is that the Memory engine has a different default compared to the other storage engines. When we designed the NDB handler we decided that any SQL users expects to have an ordered index when they create an index on a table. Since primary key indexes are always hash-based, this meant that in NDB the default primary key index is actually two indexes, one primary hash index and one secondary ordered index on the same fields.
Not so in the Memory engine. The memory engine also uses a hash-based index by default. So when you create a new index on a Memory engine and specify no type, the index wil become a hash index. Hash indexes are not very good at range queries :) so thus the surprise to me when benchmarking the Memory engine in Sysbench.
Fixing this issue required some code changes in the sysbench test. It required the proposed fix from Mark Callaghan to add secondary indexes to sysbench. This was however not sufficient since this patch only added an index by adding KEY xid (id) and didn't specifically specify this index had to be an ordered index. So I changed this to KEY xid (id) USING BTREE and off the performance went.
The Memory engine is still more or less a single threaded engine for any write workload like Sysbench RW which limits performance very much.
However the Sysbench Readonly benchmark for the Memory engine was interesting since it used no limiting locks (concurrent readers don't contend with each other). I decided to see how much scalability was achievable for a storage engine without any concurrency issues internally.
I found that performance of the Memory engine was limited to 8% more than the InnoDB engine in the same benchmark. So my interpretation of this result is that for readonly workloads, the main limiting factor on scalability is the MySQL Server scalability issues and not the InnoDB ones.
So going forward when working on further improving the scalability of the MySQL Server parts there is a perfect benchmark that can be used to see how scalable the MySQL Server part is by using the Memory engine.
I plan on also adding a feature to sysbench making it possible to use more than one Sysbench table to see how much added scalability we get when there are several tables involved in the query mix. Sysbench is a a very syntectic benchmark in this manner that it only uses one table.
Only using one table provokes more bottlenecks than is found in most normal workloads, e.g. the LOCK_open in the MySQL Server, the meta data locks introduced in MySQL 5.5, the index mutex in InnoDB and even some unbalance to the usage of multiple buffer pools come from only using one table in the benchmark.