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Announcing YugabyteDB 1.0! 🍾 🎉

Team YugaByte is delighted to announce the general availability of YugabyteDB 1.0!

It has been an incredibly satisfying experience to, in just two years, build and launch a cloud-scale, transactional and high-performance database that’s already powering real-world production workloads. I wanted to take a moment to share our journey to 1.0 and the road ahead.

The Inspiration

Modern user-facing applications are increasingly moving to a multi-region,

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Yes We Can! Distributed ACID Transactions with High Performance

ACID transactions are a fundamental building block when developing business-critical, user-facing applications. They simplify the complex task of ensuring data integrity while supporting highly concurrent operations. While they are taken for granted in monolithic SQL/relational databases, distributed NoSQL/non-relational databases either forsake them completely or support only a highly restrictive single-row flavor (see sections below). This loss of ACID properties is usually justified with a gain in performance (measured in terms of low latency and/or high throughput).

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Orchestrating Stateful Apps with Kubernetes StatefulSets

Kubernetes, the open source container orchestration engine that originated from Google’s Borg project, has seen some of the most explosive growth ever recorded in an open source project. The complete software development lifecycle involving stateless apps can now be executed in a more consistent, efficient and resilient manner than ever before. However, the same is not true for stateful apps — containers are inherently stateless and Kubernetes did not do anything special in the initial days to change that.

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Overcoming MongoDB Sharding and Replication Limitations with YugabyteDB

A few of our early users have chosen to build their new cloud applications on YugabyteDB even though their current primary datastore is MongoDB. Starting with the v3.4 release in Nov 2016, MongoDB has made improvements in its sharding and replication architecture that has allowed it to be re-classified as a Consistent and Partition-tolerant (CP) database and move away from its Available and Partition-tolerant (AP) origins. However, significant limitations remain that make it unsuitable for latency-sensitive,

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Building Scalable Cloud Services — An Instant Messaging App

Source: https://stackoverflow.com/questions/47276519/how-should-i-or-should-not-use-cassandra-and-redis-together-to-build-a-scalable

This is the first post in a series about building real-world, distributed cloud services using a transactional cloud database like YugabyteDB.

We are going to look at how to build a scalable chat or messaging application like Facebook Messages. This is close to heart to a number of us at YugaByte — we were the team behind the database platform that powers the Facebook Messages app.

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Achieving Sub-ms Latencies on Large Datasets in Public Clouds

One of our users was interested to learn more about YugabyteDB’s behavior for a random read workload where the data set does not fit in RAM and queries need to read data from disk (i.e. an uncached random read workload).

The intent was to verify if YugabyteDB was designed well to handle this case with the optimal number of IOs to the disk subsystem.

This post is a sneak peak into just one of the aspects of YugabyteDB’s innovative storage engine,

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Using Redis API as a True Distributed, Fault-Tolerant Database

YugabyteDB is an open source, transactional, high-performance database for your business-critical data —and it is compatible with the Redis API. Over the years, many of us fell in love with the simplicity and the intuitiveness of the various Redis commands and data structures. We are excited to share the same love with all the Redis developers out there.

When using your Redis application with YugabyteDB, your data is replicated and persisted with strong consistency.

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Scaling YugabyteDB to Millions of Reads and Writes

Here at YugaByte, we continuously push the limits of the systems we build. As a part of that, we ran some large cluster benchmarks to scale YugabyteDB to million of reads and writes per second while retaining low latencies. This post goes into the details about our 50 node cluster benchmark. We posted the results of the benchmark on a 25 node cluster in our community forum.

The graph above shows how you can achieve linear scalability with YugabyteDB.

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Extending Redis API with a Native Time Series Data Type

Defining time series data

We have spoken to several developers that have a need to model time series like data in Redis. A few examples of such applications are:

  • Stock quote feed.
  • Order history for a user in an online retailer.
  • User activity in any application.
  • Data gathered from IoT sensor devices.

In general time series data has the following characteristics:

  1. Each data point in a time series (e.g.

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Building a Strongly Consistent Cassandra with Better Performance

In an earlier blog on database consistency, we had a detailed discussion on the risks and challenges applications face in dealing with eventually consistent NoSQL databases. We also dispelled the myth that eventually consistent DBs perform better than strongly consistent DBs. In this blog, we will look more closely into how YugabyteDB provides strong consistency while outperforming an eventually consistent DB like Apache Cassandra. Note that YugabyteDB retains drop-in compatibility with the Cassandra Query Language (CQL) API.

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