Join Percona Chief Evangelist Colin Charles as he covers happenings, gives pointers and provides musings on the open source database community.

Much of last week, there was a lot of talk around this article: New research shows 75% of ‘open’ Redis servers infected. It turns out, it helps that one should always read beyond the headlines because they tend to be more sensationalist than you would expect. From the author of Redis, I highly recommend reading Clarifications on the Incapsula Redis security report, because it turns out that in this case, it is beyond the headline. The content is also suspect. Antirez had to write this to help the press (we totally need to help keep reportage accurate).

Not to depart from the Redis world just yet, but …

Occasionally we have customers with MyISAM storage engine approaching us to migrate their database to InnoDB, MyISAM is great if you are just an application like web content management system with no multi-user concurrency challenges but what if you are building an highly transactional data web property ? InnoDB is much preferred for such situations, InnoDB provides Row-level locking (Oracle like) for consistent reads on an multi-user concurrent user high performance database application. InnoDB also guarantees maximum data integrity by supporting FOREIGN KEY, We captured below few interesting points to remember while migrating your database from MyISAM to InnoDB :

• Data of InnoDB tables is stored in *.ibd files, deleting those files will permanently corrupt your database
• InnoDB tables consumes more storage space than MyISAM tables .
• Unlike MyISAM, InnoDB is a transactional database engine. In any typical MyISAM …

In one of the projects I have been working on, one requirement was the ability to stop traffic from reaching a MySQL host which has been lagging behind its master for longer than a specific amount of time and then bring it back online once the lag has gone away. Of course, this is all automated and no human intervention is required.

In this scenario, we are using HAProxy as the load balancer, and I will walk you through how to configure an agent so we can use HAProxy httpchk to flag the host as up or down, via systemd socket and then automatically set the host as being down/up when applicable, in HAProxy.

I will be setting up a systemd service (I’m running centos7 hosts) and creating a listening socket in the MySQL host we want to monitor so haproxy can have access to replication status.

Scenario:

master: po-mysql1
slaves: po-mysql2, po-mysql3, po-mysql4
secondary slaves: …

Generally, when I'm analyzing MySQL Performance on Linux with "localhost" test workloads, I'm configuring client connections to use IP port (loopback) to connect to MySQL Server (and not UNIX socket) -- this is still at least involving IP stack in the game, and if something is going odd on IP, we can be aware ahead about. And indeed, it already helped several times to discover such kind of problems even without network links between client/server (like this one, etc.). However, in the past we also observed a pretty significant difference in QPS results when IP port was used comparing to UNIX socket (communications via UNIX socket were going near 15% faster).. Over a time with newer OL kernel releases this gap became smaller and smaller. But in all such …

Both MongoDB and the MySQL Document Store are JSON document stores.  The syntax differences in the two products are very interesting.  This long will be a comparison of how commands differ between these two products and may evolve into a 'cheat sheet' if there is demand.

I found an excellent Mongo tutorial Getting Started With MongoDB that I use as a framework to explore these two JSON document stores.
The DataI am using the primer-dataset.json file that MongoDB has been using for years  in their documentation, classes, and examples. MySQL has created the world_x data set based on the world database used for years in documentation, classes and examples.  The data set is a collection of JSON documents filled with restaurants around Manhattan.
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I frequently talk to our customer base about what keeps them up at night. While there is a large variance of answers, they tend to fall into one of two categories. The first is the conditioned fear of some monster lurking behind the scenes that could pounce at any time. The second, of course, is the actual monster of downtime on a critical system. Ask most tech folks and they will tell you outages seem to only happen late at night or early in the morning. And that they do keep them up.

Entire companies and product lines have been built around providing those in the IT world with some ability to sleep at night. Modern enterprises have spent millions to mitigate the risk and prevent their businesses from having a really bad day because of an outage. Cloud providers are attuned to the downtime dilemma and spend lots of time, money, and effort to build in redundancy and make “High Availability” (HA) as easy as possible. The frequency of …

Next week I will be speaking at DataOps in Barcelona about MySQL 8.0 Document Store. If you don’t know it yet, I really invite you to join this talk, you will be very surprised about all MysQL can do in the NoSQL world !

There will be also a lot other MySQL related sessions by many good speakers of the MySQL Community.

As I will be in Barcelona, the Barcelona MySQL Meetup invited me to give a session about MySQL InnoDB Cluster and Group Replication and I will also share the stage with my friend and colleague …

Last time we looked at moving a JSON data set from MongoDB to the MySQL Document Store.  Let's move another and then see how to investigate this date.  We will use the primer-dataset.json that contains data on restaurants around New York City.

Loading Data
The loading of the JSON data set was covered last time but here is the gist. The first step is to fire up the MySQL Shell and login to the server.

 We need a new schema for this data and the example shows one created as nyeats.  The within that new schema a collection is created with the name restaurants.


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This Log Buffer edition covers Oracle, SQL Server and MySQL.

Cloud:

What DBAs need to know about Cloud Spanner, Part 1: Keys and indexes

Introducing sole-tenant nodes for Google Compute Engine — when sharing isn’t an option

A serverless solution for invoking AWS Lambda at a sub-minute frequency

Amazon Aurora MySQL DBA Handbook – …

A simple test to see how much faster restarts are in 7.6 compared to 7.5 is to
load a set of DBT2 warehouses and next perform a node restart of one of the
data nodes.

In the test we load 600 warehouses, this means about 60 GByte of data inserted
into NDB in each data node.

A node restart of 7.5.10 using this data set takes 19 minutes and 11 seconds.
In 7.6.6 this restart takes 6 minutes and 49 seconds.

The difference can vary, especially since the restart time in 7.5 has a fairly high
variance. The restart time in 7.6.6 has much smaller variance.

We will look into the different restart phases and see how those are affected.

The first phase is to allocate the memory and ensure that the memory is allocated
to our process by touching the memory. This phase roughly allocates 2.7 GByte
per second. In this case it takes 26 …