The Distributed SQL Blog - Page 31 of 32 - Thoughts on distributed databases, open source and cloud native.

ACID Transactions Databases Distributed SQL Google Spanner How It Works Performance Benchmarks

April 24, 2018

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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How It Works How To MongoDB

March 7, 2018

by Sid Choudhury

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.

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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 YugaByte DB.

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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How To

February 2, 2018

by Karthik Ranganathan

Amazon Web Services Cloud Providers Google Cloud Platform How It Works Microsoft Azure Performance Benchmarks

January 26, 2018

by Kannan Muthukkaruppan

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.

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ACID Transactions Amazon Web Services Distributed SQL Google Cloud Platform Google Spanner Microsoft Azure Microsoft Azure Cosmos DB

January 25, 2018

by Sid Choudhury

Practical Tradeoffs in Google Cloud Spanner, Azure Cosmos DB and YugabyteDB

Updated April 2019.

The famed CAP Theorem has been a source of much debate among distributed systems engineers. Those of us building distributed databases are often asked how we deal with it. In this post, we dive deeper into the consistency-availability tradeoff imposed by CAP which is only applicable during failure conditions.

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How It Works

January 18, 2018

by Karthik Ranganathan

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

YugaByte DB 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.

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How It Works Performance Benchmarks

January 12, 2018

by Bharat Baddepudi

Scaling YugaByte DB 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 YugaByte DB 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.

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How It Works How To

January 5, 2018

by Pritam Damania

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.

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ACID Transactions Apache Cassandra How It Works Performance Benchmarks

December 20, 2017

by Robert Pang

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 YugaByte DB provides strong consistency while outperforming an eventually consistent DB like Apache Cassandra.

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