Follower Clusters – 3 Major Use Cases for Syncing SQL & NoSQL Deployments

Follower clusters are a ScaleGrid feature that allows you to keep two independent database systems (of the same type) in sync. Unlike cloning or replication, this allows you to maintain an active, point-in-time copy of your production data. This extra cluster, known as a follower cluster, can be leveraged for multiple use cases, including for analyzing, optimizing and testing your application performance for MongoDB, MySQL and PostgreSQL. In this blog post, we will cover the top three scenarios to leverage follower clusters for your application.

How Do Follower Clusters Differ From Replication?

Unlike a static clone, this data imports on a set schedule so your follower cluster is always in sync with your production cluster. Here are a few critical ways in which it differs from replication:

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Managing High Availability in PostgreSQL – Part III: Patroni

In our previous blog posts, we discussed the capabilities and functioning of PostgreSQL Automatic Failover (PAF) by Cluster Labs and Replication Manager (repmgr) by 2ndQuadrant. In the final post of this series, we will review the last solution, Patroni by Zalando, and compare all three at the end so you can determine which high availability framework is best for your PostgreSQL hosting deployment.

• Managing High Availability in PostgreSQL – Part I: PostgreSQL Automatic Failover
• Managing High Availability in PostgreSQL – Part II: Replication Manager
• Managing High Availability in PostgreSQL – Part III: Patroni

Patroni for PostgreSQL

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Top Redis Use Cases by Core Data Structure Types

Redis, short for Remote Dictionary Server, is a BSD-licensed, open-source in-memory key-value data structure store written in C language by Salvatore Sanfillipo and was first released on May 10, 2009. Depending on how it is configured, Redis can act like a database, a cache or a message broker. It’s important to note that Redis is a NoSQL database system. This implies that unlike SQL (Structured Query Language) driven database systems like MySQL, PostgreSQL, and Oracle, Redis does not store data in well-defined database schemas which constitute tables, rows, and columns. Instead, Redis stores data in data structures which makes it very flexible to use. In this blog, we outline the top Redis use cases by the different core data structure types.

Data Structures in Redis

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2019 PostgreSQL Trends Report: Private vs. Public Cloud, Migrations, Database Combinations & Top Reasons Used

PostgreSQL is an open source object-relational database system that has soared in popularity over the past 30 years from its active, loyal, and growing community. For the 2nd year in a row, PostgreSQL has kept the title of #1 fastest growing database in the world according to the DBMS of the Year report by the experts at DB-Engines. So what makes PostgreSQL so special, and how is it being used today? We found the answers at the Postgres Conference in March where we surveyed PostgreSQL users, contributors, and SQL and NoSQL database administrators alike. In this free PostgreSQL Trends Report, we break down PostgreSQL hosting use across public cloud vs. private cloud vs. hybrid cloud, most popular cloud providers, migration trends, database combinations with Postgres, and why PostgreSQL is preferred over popular RDBMS alternatives.

Private Cloud vs. Public Cloud vs. Hybrid Cloud

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Intro to Redis Cluster Sharding – Advantages, Limitations, Deploying & Client Connections

Redis Cluster is the native sharding implementation available within Redis that allows you to automatically distribute your data across multiple nodes without having to rely on external tools and utilities. At ScaleGrid, we recently added support for Redis Clusters on our platform through our fully managed Redis hosting plans. In this post, we’re going to introduce you to the advanced Redis Cluster sharding opportunities, discuss its advantages and limitations, when you should deploy, and how to connect to your Redis Cluster.

Sharding with Redis Cluster

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Open Source Database HA Resources from Severalnines

Severalnines has spent the last several years writing blogs and crafting content to help make your open source database solutions highly available. We are fans of highscalability.com and wanted to post some links to our top resources to help readers learn more how to make MySQL, MongoDB, MariaDB, Percona and PostgreSQL databases scalable.

Top HA Resources for MySQL & MariaDB

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NoCAP

In this post i wanted to spend sometime on the CAP theorem and clarify some of the confusion that i often see when people associate CAP with scalability without fully understanding the implications that comes with it and the alternative approaches

You can read the full article here

Need help with your Hadoop deployment? This company may help!

A group of top Silicon Valley engineers (ex-Yahoo, Facebook, Google) have come together to launch a new startup called Cloudera. Not yet launched, it intends to help other companies adopt a promising software platform called Hadoop.

Hadoop is an open-source software project (written in Java) designed to let developers write and run applications that process huge amounts of data. While it could potentially improve a wide range of other software, the ecosystem supporting its implementation is still developing. Which is where Cloudera hopes to make a place for itself.

More on Hadoop: It uses the Google-introduced MapReduce systems framework that divides applications into small blocks of work, creating multiple replicas of data blocks that it places on various computer nodes.

It is already in use at large companies like Yahoo.

Read more about Cloudera here.

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Product: Terracotta - Open Source Network-Attached Memory

Update: Evaluating Terracotta by Piotr Woloszyn. Nice writeup that covers resilience, failover, DB persistence, Distributed caching implementation, OS/Platform restrictions, Ease of implementation, Hardware requirements, Performance, Support package, Code stability, partitioning, Transactional, Replication and consistency. Terracotta is Network Attached Memory (NAM) for Java VMs. It provides up to a terabyte of virtual heap for Java applications that spans hundreds of connected JVMs. NAM is best suited for storing what they call scratch data. Scratch data is defined as object oriented data that is critical to the execution of a series of Java operations inside the JVM, but may not be critical once a business transaction is complete. The Terracotta Architecture has three components:

1. Client Nodes - Each client node corresponds to a client node in the cluster which runs on a standard JVM
2. Server Cluster - java process that provides the clustering intelligence. The current Terracotta implementation operates in an Active/Passive mode
3. Storage used as
   • Virtual Heap storage - as objects are paged out of the client nodes, into the server, if the server heap fills up, objects are paged onto disk
   • Lock Arbiter - To ensure that there is no possibility of the classic "split-brain" problem, Terracotta relies on the disk infrastructure to provide a lock.
   • Shared Storage - to transmit the object state from the active to passive, objects are persisted to disk, which then shares the state to the passive server(s).

JVM-level clustering can turn single-node, multi-threaded apps into distributed, multi-node apps, often with no code changes. This is possible by plugging in to the Java Memory Model in order to maintain key Java semantics of pass-by-reference, thread coordination and garbage collection across the cluster. Terracotta enables this using only declarative configuration with minimal impact to existing code and provides fine-grained field-level replication which means your objects no longer need to implement Java serialization. Ari Zilka, the founder and CTO of Terracotta had a video session organized by Skills Matter. He will show you how it works and how you can start clustering your POJO-based Web applications (based on Spring, Struts, Wicket, RIFE, EHCache, Quartz, Lucene, DWR, Tomcat, JBoss, Jetty or Geronimo etc.).

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Database-Clustering: a8cjdbc - update: version 1.3

The new version of a8cjdbc finished some limitations. Now Clobs and Blobs are supported, and some fixes using binary data. The version was also fully tested with Postgres and mySQL. Since Version 1.3 there is also a free trail version for download available. Check it out and test yourself... Take a look at: http://www.activ8.at/homepage/en/a8cjdbc.php I've downloaded the latest version and setup a environment with one virtual database and two database backends. I tried to make a "non real life szenario": The first backend was a Postgres node, the second was a mySQL node. Everything works fine - failover - recoverylog, etc... with to different backend database types. So check out the trial version and test yourself the clustered driver and give me some results about your experience with a8cjdbc. As I only tested mySQL and Postgres (and the non real life szenario with two different backend types) - maybe someone else have experiences with out databases? greetings Wolfgang

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