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The standard redundant Redis solution is to run master/slave replication with
Sentinel managing the failover. This is expected to be followed up with either
a) Client support and use of Sentinel to discover the current master or b) A
TCP proxy in front of the Redis pod which is managed by Sentinel to point to
the master. The former is the ways Redis Sentinel is designed and the latter is
a growing trend - and the way ObjectRocket Redis is configured.
If you're looking to transform your millions (or billions!) of Elasticsearch documents into eye-catching charts, graphs, and tables, Kibana is the answer. As an Elastic product, it integrates closely with Elasticsearch and provides an easy-to-use interface for making sense of mounds of data.
When a company's customers, employees, and partners can access data easily through a user-friendly system, they have two people to thank for it: a database administrator and a data architect. Ensuring that well-built databases function reliably and securely for potentially thousands or even millions of users is a major responsibility, and companies in every industry rely on data architects and DBAs to design and monitor data networks that meet the needs of all who use them.
Redis is hot in the tech community right now. It's come a long way from being a small personal project from Antirez, to being an industry standard for in memory data storage. With that comes a set of best practices that most people can agree upon for using Redis properly. Below we'll explore 10 quick tips on using Redis correctly.
Redis Sentinel provides a simple and automatic high availability (HA) solution for Redis. If you’re familiar with how MongoDB elections work, this isn’t too far off. To start, you have a given master replicating to N number of slaves. From there, you have Sentinel daemons running, be it on your application servers or on the servers Redis is running on. These keep track of the master’s health.
Redis uses a very straightforward command line interface. Though it's relatively simple, it does provide some interesting features that one might not expect. Let's go over some of the basics and work our way around most of the client's functionality and features.
Hashes in Redis are a way to store associated field-value pairs under a single key, where both the field and values are strings. Redis allows for modifications to both the data structure as a whole, and also to each field in the structure. This makes it a great (and very fast) backing store for objects in an application.
In Matthew Barker's Getting started with Redis post, we covered the 5 data structures in Redis. This overview will cover some key operations and common uses, along with security options and a few simple example scripts.
Recently, the Rackspace DevOps Automation team announced a service that sends alerts from New Relic to Rackspace support. These alerts will generate tickets for our DevOps Engineers to respond to, so our customers can sleep soundly when alerts are generated at 3am. When combined with other data points collected about our customers’ environments, our Engineers will identify where issues lie and then execute the proper course of action.
As I am afforded the privilege of speaking with many people and companies using Redis in a variety of use cases from simple caching to multi-terabyte sized setups the one topic I am asked to address more than any other is performance. Redis is different in how you approach performance. In many, if not most, database servers you try to improve performance. With Redis the goal is to not slow it down. This is a very different approach and requires a different mindset to take advantage of it.
The speed and flexibility of Redis makes it an extremely powerful tool for developers and it can be used in a variety of different ways. Although Redis is often referred to as a key-value store it is much better described as a Data Structure Server, as it also supports 5 different data structure types, namely:
For those of you new to using MongoDB, MongoDB space usage can seem quite confusing. In this article, I will explain how MongoDB allocates space and how to interpret the space usage information in our ObjectRocket dashboard to make judgements about when you need to compact your instance or add a shard to grow the space available to your instance.
Appboy is the world's leading marketing automation platform for mobile apps. We collect billions of data points each month by tracking what users are doing in our customers' mobile apps and allowing them to target users for emails, push notifications and in-app messages based on their behavior or demographics. MongoDB powers most of our database stack, and we host dozens of shards across multiple clusters at ObjectRocket.
At MongoDB World last month MongoDB founder and CTO Eliot Horowitz announced support for pluggable storage engines scheduled for the 2.8 release. This is exciting stuff as it means mongo users will now be able to choose a storage engine that best suits their workload and with the API planned to have full support of All MongoDB features, while not having to give up any of the current functionality that they enjoy. Not only that, but nodes in the same replica set will be able to use different storage engines, enabling all sorts of interesting configurations for varying needs.
MongoDB Inc. has introduced lots of great new enterprise features with release 2.6 of MongoDB, however, one thing still absent is a desktop application to manage your database. Introducing Robomongo, the cross-platform and open source MongoDB management tool. With the following instructions you'll see how easy it is to integrate RoboMongo with your ObjectRocket MongoDB instance.