This is a hypothesis, perhaps it is true. I am biased given that I spent 15 years acknowledging tech debt on a vendor-based project (MySQL) and not much time on community-based projects.

My theory on tech debt and OSS is that there is more acknowledgement of tech debt in vendor-based projects than community-based ones. This is an advantage for vendor-based projects assuming you are more likely to fix acknowledged problems. Of course there are other advantages for community-based projects.

I think there is more acknowledgement of tech debt in vendor-based projects because the community is criticizing someone else's effort rather than their own. This is human nature, even if the effect and behavior aren't always kind. I spent many years marketing bugs that needed to be fixed -- along with many years  leading teams working to fix those bugs.

MySQL Cluster 8.0.18 RC2 was released a few weeks back packed with a set of
new interesting things.

One major change we have done is to increase the number of data nodes from 48 to
144. There is also ongoing work to fully support 3 and 4 replicas in MySQL
Cluster 8.0. NDB has actually always been designed to handle up to 4 replicas.
But the test focus have previously been completely focused on 2 replicas. Now we
expanded our test focus to also verify that 3 and 4 replicas work well. This means
that with NDB 8.0 we will be able to confidently support 3 and 4 replicas.
This means that with NDB 8.0 it will be possible to have 48 node
groups with 3 replicas in each node group in one cluster.

The higher number of nodes in a cluster gives the possibility to store even more
data in the cluster. So with 48 node groups it is possible to store 48 TByte of
…

I am wary of papers with performance results for too many products.Too many means including results from systems for which you lack expertise. Wary means I have less faith in the comparison even when the ideas in the paper are awesome. I have expertise in MySQL, MongoDB, RocksDB, WiredTiger and InnoDB but even for them I have made and acknowledged ridiculous mistakes.

Database configuration is too hard. There are too many options, most of them aren't significant and the approach is bottom-up. I an expert on this -- in addition to years of tuning I have added more than a few options to RocksDB and MySQL.

This post was motivated by PostgreSQL. I want to run the insert benchmark for it and need a good configuration. I have nothing against PG with the exception of a few too many why …

To illustrate how easy it’s to see who’s trying to access data they have not been granted for, we will first create a schema with two tables:

mysql> create database mydata;
mysql> use mydata
mysql> create table table1 (id int auto_increment primary key, 
              name varchar(20), something varchar(20));
mysql> create table table2 (id int auto_increment primary key, 
              name varchar(20), something varchar(20));

Now, let’s create a user :

mysql> create user myuser identified by 'mypassword';

And as it’s always good to talk about SQL ROLES, let’s define 3 roles for our user:

• myrole1: user has access to both tables in their entirety, reads and writes
• myrole2: user has access only to `table2`, reads and writes
• myrole3: user has only access to the column `name`of `table1` …

At Oracle’s OpenWorld 2019, Oracle announced an Oracle Cloud Free Tier, which includes Oracle Cloud services that will always be free – as long as you use them or keep them active.

Most other hyperscale cloud vendors offer a free 12-month trial before they start charging you, so to see Oracle provide a “truly free” service is really great. And the free services are not just limited to one or two choices – you get a wide variety of free services to choose from. As of this blog post, here is a brief summary of free services (these are subject to change in the future):

Databases – Your choice of Autonomous Transaction Processing …

MySQL InnoDB Cluster consists of 3 components:

• MySQL Group Replication (a group of database server which replicates to each other with fault tolerance).
• MySQL Router (query router to the healthy database nodes)
• MySQL Shell (helper, client, configuration tool)

In the first part of this walkthrough, we are going to deploy a MySQL InnoDB Cluster. There are a number of hands-on tutorial available online but this walkthrough covers all the necessary steps/commands to install and run the cluster in one place. We will be covering monitoring, management and scaling operations as well as some gotchas when dealing with MySQL InnoDB Cluster in the second part of this blog post.

The following diagram illustrates our post-deployment architecture:

We are going to deploy a total of 4 nodes; A three-node MySQL Group Replication and one MySQL router node co-located within the application …

In my previous blog, I have explained how the MySQL clone plugin works internally. In this blog, I am going to do a comparison of  Backup and Recovery speed of MySQL clone plugin with other available mysql open source backup tools.

Below tools are used for speed comparison of Backup and Recovery,

1. Clone-Plugin
2. Xtrabackup
3. mysqldump
4. mydumper with myloader
5. mysqlpump

Test …

For a long time, the only algorithm for executing a join in MySQL has been variations of the nested loop algorithm. With the release of MySQL 8.0.18, the server can now execute joins using hash join. This blog post will have a look at how it works, when it is used, and how it compares to the old join algorithms in MySQL in terms of performance.…

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Recently, at Percona Live Europe 2019, Dimitri Kravchuk from Oracle mentioned that he observed some unclear drop in performance for MySQL on an ext4 filesystem with the latest Linux kernels. I decided to check this case out on my side and found out that indeed, starting from linux kernel 4.9, there are some cases with notable (up to 2x) performance drops for ext4 filesystem in direct i/o mode.

So what’s wrong with ext4? It started in 2016 from the patch that was pushed to kernel 4.9: “ext4: Allow parallel DIO reads”. The purpose of that patch was to help to improve read scalability in direct i/o mode. However, along with improvements in pure read workloads, it also introduced regression in intense mixed random read/write scenarios. And it’s quite weird, but this issue had not been …

How do you use an LSM for analytics? I haven't thought much about it because my focus has been small data -- web-scale OLTP since 2006. It is a great question given that other RocksDB users (Yugabyte, TiDB, Rockset, CockroachDB) support analytics.

This post isn't a list of everything that is needed for an analytics engine. That has been described elsewhere. It is a description of problems that must be solved when trying to use well-known techniques with an LSM. I explain the LSM challenges for a vectorized query engine, columnar storage, analytics indexes and bitmap indexes.

There is much speculation in this post. Some of it is informed -- I used to maintain bitmap indexes at Oracle. There is also big opportunity for research and for R&D in this space. I am happy to be corrected and be told of papers that I should read.

Vectorized query engine

See …