Redis Cloud Gets Easier with Fully Managed Hosting on Azure

ScaleGrid, a rapidly growing leader in the Database-as-a-Service (DBaaS) space, has just launched their new fully managed Redis on Azure service. This Redis management solution allows startups up to enterprise-level organizations automate their Redis operations on Microsoft Azure dedicated cloud servers, alongside their other open source database deployments, including MongoDB, MySQL and PostgreSQL.

Redis, the #1 key-value store and top 10 database in the world, has grown by over 300% in popularity over that past 5 years, per the DB-Engines knowledge base. The demand for Redis is skyrocketing across dozens of use cases, particularly for cache, queues, geospatial data, and high speed transactions. This simple database management system makes it very easy to store and retrieve pairs of keys and values, and is commonly paired with other database types to increase the speed and performance of an application. According to the 2019 Open Source Database Report, a majority of Redis deployments are used in conjunction with MySQL, and over half of Redis deployments are used with either PostgreSQL, MongoDB, and Elasticsearch.

ScaleGrid’s Redis hosting service allows these organizations to automate all of their time-consuming management tasks, such as backups, upgrades, scaling, replication, sharding, monitoring, alerts, log rotations, and OS patching, so their DBAs, developers, and DevOps teams can focus on new product development and optimizing performance. Additionally, organizations can customize their Redis persistence and host through their own Azure account which allows them to leverage advanced cloud capabilities like Azure Virtual Networks (VNET), Security Groups, and Reserved Instances to reduce long-term hosting costs up to 60%. 

“Cloud reliability has never been so important,” says Dharshan Rangegowda, Founder and CEO of ScaleGrid. “It’s crucial for organizations to properly configure their Redis deployments for high availability and disaster recovery, as a couple minutes of downtime can be detrimental to a company’s security and reputation.”

ScaleGrid is the only Redis cloud service that allows you to customize your master-slave and cross-datacenter configurations for 100% uptime and availability across 30 different Azure regions. They also allow you to keep full Redis admin access and SSH access to your machines, and you can learn more about their advantages over competitors Compose for Redis, RedisGreen, Redis Labs and Elasticache for Redis on their Compare Redis Providers page.

2019 Open Source Database Report: Top Databases, Public Cloud vs. On-Premise, Polyglot Persistence

Ready to transition from a commercial database to open source, and want to know which databases are most popular in 2019? Wondering whether an on-premise vs. public cloud vs. hybrid cloud infrastructure is best for your database strategy? Or, considering adding a new database to your application and want to see which combinations are most popular? We found all the answers you need at the Percona Live event last month, and broke down the insights into the following free trends reports:

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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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Homegrown master-master replication for a NoSQL database

Many of you may have already heard about the high performance of the Tarantool DBMS, about its rich toolset and certain features. Say, it has a really cool on-disk storage engine called Vinyl, and it knows how to work with JSON documents. However, most articles out there tend to overlook one crucial thing: usually, Tarantool is regarded simply as storage, whereas its killer feature is the possibility of writing code inside it, which makes working with your data extremely effective. If you’d like to know how igorcoding and I built a system almost entirely inside Tarantool, read on.

If you’ve ever used the Mail.Ru email service, you probably know that it allows collecting emails from other accounts. If the OAuth protocol is supported, we don’t need to ask a user for third-party service credentials to do that — we can use OAuth tokens instead. Besides, Mail.Ru Group has lots of projects that require authorization via third-party services and need users’ OAuth tokens to work with certain applications. That’s why we decided to build a service for storing and updating tokens.

I guess everybody knows what an OAuth token looks like. To refresh your memory, it’s a structure consisting of 3–4 fields:

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In-memory noSQL DBMS Client in Big Data Cluster

This is guest post by Sergei Sheinin, creator of the 2DX Web UI Database Cluster Framework, a low latency big data cluster with in-memory noSQL DBMS Web Browser client.

When I began working in the field of data management the disconnect between rigid structure of relational database tables and free form of documents managed by end users and their businesses stood out as a technical and managerial hurdle. On the one hand there were strict definitions of normalized relational database models and unstructured document formats on the other. Often the users in charge of changing document structures held organizational responsibilities far removed from database modeling or programming. On one occasion I was involved in a project where call center operators made on the fly decisions to update a document structure based on phone conversations with customers. Such updates had to be streamed into a relational back-end creating havoc in database structure and build of table columns.

In seeking a permanent solution I researched merits of Entity-Attribute-Value database schema and its applications. This technique proved successful in enabling front end users to modify relational-bound documents through performing updates to structure described in their metadata. However application of EAV raised its own issues, for example accommodation of updated document metadata at times required changes to definitions of the relational tables, attention of developers due to complexity of application layer in client-server interoperability, rapidly growing fact tables and performance of multiple join statements in select queries...

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Datanet: a New CRDT Database that Let's You Do Bad Bad Things to Distributed Data

We've had databases targeting consistency. These are your typical RDBMSs. We've had databases targeting availability. These are your typical NoSQL databases.

If you're using your CAP decoder ring you know what's next...what databases do we have that target making concurrency a first class feature? That promise to thrive and continue to function when network partitions occur?

No many, but we have a brand new concurrency oriented database: Datanet - a P2P replication system that utilizes CRDT algorithms to allow multiple concurrent actors to modify data and then automatically & sensibly resolve modification conflicts.

Datanet is the creation of Russell Sullivan. Russell spent over three years hidden away in his mad scientist layer researching, thinking, coding, refining, and testing Datanet. You may remember Russell. He has been involved with several articles on HighScalability and he wrote AlchemyDB, a NoSQL database, which was acquired by Aerospike.

So Russell has a feel for what's next. When he built AlchemyDB he was way ahead of the pack and now he thinks practical, programmer friendly CRDTs are what's next. Why?

Concurrency and data locality. To quote Russell:

Datanet lets you ship data to the spot where the action is happening. When the action happens it is processed locally, your system's reactivity is insanely quick. This is pretty much the opposite of the non-concurrent case where you need to go to a specific machine in the cloud to modify a piece of data regardless of where the action takes place. As your system grows, the concurrent approach is superior.

We have been slowly moving away from transactions towards NoSQL for reasons of scalability, availability, robustness, etc. Datanet continues this evolution by taking the next step and moving towards extreme distribution: supporting tons of concurrent writers.

The shift is to more distribution in computation. We went from one app-server & one DB to app-server-clusters and clustered-DBs, to geographically distributed data-centers, and now we are going much further with Datanet, data is distributed anywhere you need it to a local cache that functions as a database master.

How does Datanet work?

In Datanet, the same piece of data can simultaneously exist as a write-able entity in many many places in the stack. Datanet is a different way of looking at data: Datanet more closely resembles an internet routing protocol than a traditional client-server database ... and this mirrors the current realities that data is much more in flight than it used to be.

What bad bad things can you do to your distributed data? Here's an amazing video of how Datanet recovers quickly, predictably, and automatically from Chaos Monkey level extinction events. It's pretty slick. 

Here's an email interview I did with Russell. He goes into a lot more detail about Datanet and what it's all about. I think you will find it interesting. 

Let's start with your name and a little of your background?

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RebornDB: the Next Generation Distributed Key-Value Store

There are many key-value stores in the world and they are widely used in many systems. E.g, we can use a Memcached to store a MySQL query result for later same query, use MongoDB to store documents for better searching, etc.

For different scenarios, we should choose different key-value store. There is no silver-bullet key-value store for all solutions. But if you just want a simple key-value store, easy to use, very fast, supporting many powerful data structures, redis may be a good choice for your start.  

Redis is advanced key-value cache and store, under BSD license. It is very fast, has many data types(String, Hash, List, Set, Sorted Set …), uses RDB or AOF persistence and replication to guarantee data security, and supplies many language client libraries.

Most of all, market chooses Redis. There are many companies using Redis and it has proved its worth.

Although redis is great, it still has some disadvantages, and the biggest one is memory limitation.  Redis keeps all data in memory, which limits the whole dataset size and lets us save more data impossibly.

The official redis cluster solves this by splitting data into many redis servers, but it has not been proven in many practical environments yet. At the same time, it need us to change our client libraries to support “MOVED” redirection and other special commands, this is unacceptable in running production too. So redis cluster is not a good solution now.

QDB

We like redis, and want to go beyond its limitation, so we building a service named QDB, which is compatible with redis, saves data in disk to exceed memory limitation and keeps hot data in memory for performance.

Introduction

QDB is a redis like, fast key-value store.It has below good features:

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NoSQL Style - A Gangnam Style Parody

Listen up all you IT people...NoSQL, it's the rage now, so turn the page now and boost your stack...Hey, mighty people...Go, go, go, hey, hey, hey, hey, hey, hey...Go NoSQL style...

I for one feel both edified and entertained...can't wait for the Harlem Shake version. 

Google Spanner's Most Surprising Revelation: NoSQL is Out and NewSQL is In

Google recently released a paper on Spanner, their planet enveloping tool for organizing the world’s monetizable information. Reading the Spanner paper I felt it had that chiseled in stone feel that all of Google’s best papers have. An instant classic. Jeff Dean foreshadowed Spanner’s humungousness as early as 2009.  Now Spanner seems fully online, just waiting to handle “millions of machines across hundreds of datacenters and trillions of database rows.” Wow.

The Wise have yet to weigh in on Spanner en masse. I look forward to more insightful commentary. There’s a lot to make sense of. What struck me most in the paper was a deeply buried section essentially describing Google’s motivation for shifting away from NoSQL and to NewSQL. The money quote:

We believe it is better to have application programmers deal with performance problems due to overuse of transactions as bottlenecks arise, rather than always coding around the lack of transactions.

This reads as ironic given Bigtable helped kickstart the NoSQL/eventual consistency/key-value revolution.

We see most of the criticisms leveled against NoSQL turned out to be problems for Google too. Only Google solved the problems in a typically Googlish way, through the fruitful melding of advanced theory and technology. The result: programmers get the real transactions, schemas, and query languages many crave along with the scalability and high availability they require.

The full quote:

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