Benchmarking MyRocks vs. InnoDB in Memory-Constrained Environments It is a well-known fact in the database world that InnoDB is incredibly fast when the entire database fits into memory. But what happens when your data grows beyond your available RAM? MyRocks, built on RocksDB, is frequently recommended as a superior choice for environments constrained by memory, […]

Bloom filters are an essential component of an LSM-based database engine like MyRocks. This post will illustrate through a simple example how bloom filters work in MyRocks.

Why?

With MyRocks/RocksDB, data is stored in a set of large SST files.  When MyRocks needs to find the value associated with a given key, it uses a bloom filter to guess if the key could potentially be in an SST file.

How?

A bloom filter is a space-efficient way of storing information about a list of keys. At its base, there is a bitmap and a hash function.  The hash value of the keys stored in an SST is computed, and the results are used to set some bits to “1” in the bitmap.  When you want to know if a key is present or not in the list, you run it through the hash function and check if the corresponding bits in the bitmap are …

I wrote this post on MyRocks because I believe it is the most interesting new MySQL storage engine to have appeared over the last few years. Although MyRocks is very efficient for writes, I chose a more generic workload that will provide a different MyRocks use case.

The use case is the TPC-C benchmark but executed not on a high-end server but on a lower-spec virtual machine that is I/O limited like for example, with AWS EBS volumes. I decided to use a virtual machine with two CPU cores, four GB of memory, and storage limited to a maximum of 1000 IOPs of 16KB. The storage device has performance characteristics pretty similar to an AWS gp2 EBS volume of about 330 GB in size. I emulated these limits using the KVM iotune settings in my lab.

<iotune>
     <total_iops_sec>1000</total_iops_sec>
     <total_bytes_sec>16384000</total_bytes_sec> …

Raspberry PI is a small single-board computer (SBCs) developed by the Raspberry Pi Foundation in association with Broadcom. This tiny computer is extremely popular and widely used in many areas. Thanks to its size, low cost, and low energy requirements, it can be used to collect data in remote locations or from sensor devices. We often need to be able to store large amounts of data efficiently on these devices.

MyRocks is a MySQL engine that uses RocksDB to store data. It is space efficient and able to handle writes quite efficiently.

First things first

Building and installing Percona Server for MySQL with MyRocks engine enabled is easy but requires some time. It is essential to make sure that you have all the ingredients and meet all the requirements:

• Raspberry PI 3, 4, 400, or superior.
• SD Card with …

As database footprints continue to explode, many companies are looking for ways to deal with such rapid growth.  One approach is to refactor traditional relational databases to fit into a NoSQL engine, where horizontal scalability is easier.  However, in many cases, this is in no way a trivial undertaking.

Another approach that has been gaining interest is the use of MyRocks as an alternative storage engine to the traditional InnoDB.  While not for everyone, in certain use cases it could be a potential solution.  As with so many things open source, the next standard questions are: which version should I use?  Any differences with the engine if I use MyRocks with MySQL 5.7 vs 8.0?

In this post, I wanted to touch on this and give some high-level thoughts on MyRocks when it comes to the version of MySQL.

…

Percona Technical Account Managers get the privilege of working with some of our largest enterprise clients day in and day out.  As such, we get to really focus on how to best leverage our technology to generate measurable benefits for our users.  While it is fun to “nerd out” and always strive to use the latest and greatest, we need to stay focused on demonstrating business value and a genuine need.  Over the past few months, I’ve been working with one of my larger clients, Dropbox, along with our professional services team to validate the use of Percona Server for MySQL with the MyRocks storage engine over a large portion of their MySQL infrastructure.

Please note – this is not meant to be a deep dive into the technical details around …

In this talk, we’ll walk through RocksDB technology and look into situations where MyRocks is a good fit versus other engines such as InnoDB. We will go over internals, benchmarks, and the tuning of MyRocks engine. We will also explore the benefits of using MyRocks within the MySQL ecosystem. Attendees will leave with solid knowledge of the latest development tools and integrations within MySQL.

Please join Alkin Tezuysal and Sergey Kuzmichev on Monday, March 23rd, at 9:00 am EDT for their webinar “When is MyRocks a Good Fit?”

View the Recording

(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 …