There are several ways to use less SSD for an OLTP workload: choose a database engine has less space amplification, store less data, move the cold data elsewhere. The first approach is a plan while the others are goals. A plan is something you can implement. A goal requires a plan to get done.

This matters when you want to decrease the cost of storage for a database workload but not everyone needs to do that. The first approach assumes your DBMS supports an LSM with leveled compaction and compression (MariaDB and Percona Server include MyRocks, ScyllaDB and Cassandra are also options). The second approach, store less data, assumes you can get everyone to agree to remove data and that is a hard conversation.

The third approach, move the cold data elsewhere, is a wonderful goal. I wonder how often that goal …

The Learned Indexes paper opened a new area of research for storage engines. The idea is to use the distribution of the data to make searches faster and/or reduce the size of search structures. Whether ML will be used is an implementation artifact to me. I am in this for the efficiency gains -- reducing space and CPU read amplification. I prefer to call this topic learned search structures rather than learned indexes because the public isn't ready for learned recovery and learned consistency.

While the Learned Indexes paper has ideas for hash maps and bloom filters most research has focused on a B-Tree. I expect learned indexes to work better with an LSM because its search structures (block indexes) are created off line with respect to user operations. There is more time for analysis and …

I am optimistic about the Open Core Summit. It can be something that benefits users, startups, the source-available community and the OSS crowd. The summit has many of the important people from the open core community. It is an opportunity for them to collaborate -- form a foundation, reduce open core license proliferation, discuss the next license to be reviewed by OSI and most importantly -- fix the open core marketing approach.

I appreciate that startups put so much time and money into building interesting system software that is shared either as OSS or source-available. I haven't enjoyed much of the marketing about the challenges that cloud creates for VC-funded …

(In the previous post, Part 6, we covered Replication.)

In this final blog post, we conclude our series of exploring MyRocks by taking a look at use case considerations. After all, having knowledge of how an engine works is really only applicable if you feel like you’re in a good position to use it.

Advantages of MyRocks

Let’s start by talking about some of the advantages of MyRocks.

Compression

MyRocks will typically do a good job of reducing the physical footprint of your data. As I mentioned in my previous post in this series about compression, you have the ability to configure compression down to the individual compaction layers for each column family. You also get the advantage of the fact that data isn’t updated once it’s written to disk. Compaction, which was …

(In the previous post, Part 5, we covered Data Reads.)

In this blog post, we continue our series of exploring MyRocks mechanics by looking at the configurable server variables and column family options. In our last post, I explained at a high level how reads occur in MyRocks, concluding the arc of covering how data moves into and out of MyRocks. In this post, we’re going to explore replication with MyRocks, more specifically read-free replication.

Some of you may already be familiar with the concepts of read-free replication as it was a key feature of the TokuDB engine, which leveraged fractal tree indexing. TokuDB was similar to MyRocks in the sense that it had a pseudo log-based storage …

(In the previous post, Part 4, we covered Compression and Bloom Filters)

In this blog post, we continue on our series of exploring MyRocks mechanics by looking at the configurable server variables and column family options. In our last post, I explained at a high level how compression and bloom filtering are applied to data files as they are initially flushed from immutable memtables and are subsequently passed through the compaction process. With that being covered, we should now have a clear understanding as to how data writing works in MyRocks and can start reviewing how data read requests are handled.

The Read Process

Let’s start off by talking about how read processes are handled at the file level. When a read request comes in, the first thing it needs to do is pull the …

Xtrabackup now supports Hotbackup for Myrocks!! yes you heard me right, this is one of the most awaited features with xtrabackup. With the latest release of percona xtrabackup 8.0.6 this is enabled and is supported only for Percona Server version 8.0.15-6 or higher, you can see detailed released notes here.

Myrocks is getting much of the attention now because of its much improved write capabilities and compression. We have also planned to have detailed blog on Myrocks features and limitations.

We shall proceed to test the backup and restore of Myrocks

Environment:

OS : Debian GNU/Linux 9 …

The RUM Conjecture asserts that an index structure can't be optimal for all of read, write and space. I will ignore whether optimal is about performance or efficiency (faster is better vs efficient-er is better). I want to use CRUM in place of RUM where C stands for database cache.

The C in CRUM is the amount of memory per key-value pair (or row) the DBMS needs so that either a point query or the first row from a range query can be retrieved with at most X storage reads. The C can also be reported as the minimal database : memory ratio to achieve at most X storage reads per point query.

My points here are:

• There are …

(In the previous post, Part 2, we covered Initial Data Flushing.)

In this blog post, we continue our series of exploring MyRocks mechanics by looking at the configurable server variables and column family options. In our last post, I explained at a high level how data moves from immutable memtables to disk. In this post, we’re going to talk about what happens to that data as it moves through the compaction process.

What is Compaction?

One of the philosophies of MyRocks is “write the data quickly and sort out data organization later”, which is pretty far removed from engines like InnoDB that take the approach of “continuously organize data on disk so it’s optimal as soon as possible”. MyRocks implements its philosophy in a way that is heavily reliant on a process …

(In the previous post, Part 1, we covered Data Writing.)

In this blog post, we continue on our series of exploring MyRocks mechanics by looking at configurable server variables and column family options. In our last post, I explained at a high level how data first entered memory space and in this post, we’re going to pick up where we left off and talk about how the flush from immutable memtable to disk occurs. We’re also going to talk about how newly created secondary indexes on existing tables get written to disk.

We already know from our previous post in the series that a flush can be prompted by one of several events, the most common of which would be when an active memtable is filled to its maximum capacity and is rotated into immutable status.

When your immutable memtable(s) is ready …