Severalnines Blog
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8 blog posts in 13 categories

A Guide to the MariaDB Columnstore for MySQL Admins

Big data processing and data warehousing plays an important role in analytics, forecasting, statistical reports, and more. This blog will help MySQL DBA's leverage the MariaDB ColumnStore (a columnar-storage engine system which is part of MariaDB AX) for these types of tasks.

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A Review of the New Analytic Window Functions in MySQL 8.0

This blog provides an overview of MySQL window functions. A window function performs an aggregate-like operation on a set of query rows. However, whereas an aggregate operation groups query rows into a single result row, a window function produces a result for each query row.

Hybrid OLTP/Analytics Database Workloads: Replicating MySQL Data to ClickHouse

Columnar data stores provide much better performance for analytics queries than regular relational databases like MySQL. ClickHouse is an example of such datastore, queries that take minutes to execute in MySQL would take less than a second instead. In this blog post we show how to tackle the challenge of replicating the data from MySQL to ClickHouse.

Analytics with MariaDB AX - the Open Source Columnar Datastore

What is a columnar datastore? When is it a better fit over a relational datastore? In this blog post, we will take a look at MariaDB AX and how it fits in this landscape.

Understanding the MySQL Galera Cluster and MariaDB Cluster Diagnostic Logs

In a Galera cluster, whether you use MariaDB Cluster, Percona XtraDB Cluster or the Codership build, the MySQL error log is even more important. It gives you the same information as MySQL would, but you’ll also find in it information about Galera internals and replication. This data is crucial to understand the state of your cluster and to identify any issues which may impact the cluster’s ability to operate. In this post, we’ll try to make the Galera error log easier to understand.

Monitoring Host Metrics of Your Database Instances - How to Interpret Operating System Data

Monitoring the metrics of the database hosts is critical. By keeping Swap and Physical Memory usage within allowable limits, you’ll ensure that there is enough memory for queries to be executed and connections to be created. By monitoring Disk Utilization, you can map growth patterns and better plan for capacity upgrades.

Big Data Integration & ETL - Moving Live Clickstream Data from MongoDB to Hadoop for Analytics

MongoDB is great at storing clickstream data, but using it to analyze millions of documents can be challenging. Hadoop provides a way of processing and analyzing data at large scale. Since it is a parallel system, workloads can be split on multiple nodes and computations on large datasets can be done in relatively short timeframes. MongoDB data can be moved into Hadoop using ETL tools like Talend or Pentaho Data Integration (Kettle).

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Archival and Analytics - Importing MySQL Data Into a Hadoop Cluster Using Sqoop

We won’t bore you with buzzwords like volume, velocity and variety. This post is for MySQL users who want to get their hands dirty with Hadoop, so roll up your sleeves and prepare for work. Why would you ever want to move MySQL data into Hadoop? One good reason is archival and analytics. You might not want to delete old data, but rather move it into Hadoop and make it available for further analysis at a later stage.