Category: How It Works

August 1, 2018

A Quick Guide to Secondary Indexes in YugabyteDB

When creating a Cassandra-compatible YCQL table in the YugabyteDB database, you are required to create a primary key consisting of one or more columns of the table. Primary key based retrievals are efficient because YugabyteDB automatically indexes/organizes the data by the primary key. However, there are many use cases where you may need to retrieve data using columns that are not a part of the primary key. This is where secondary indexes help.

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6 Signs You Might be Misunderstanding ACID Transactions in Distributed Databases

July 22, 2018

By Sid Choudhury

ACID Transactions How It Works

6 Signs You Might be Misunderstanding ACID Transactions in Distributed Databases

As described in A Primer on ACID Transactions, first generation NoSQL databases dropped ACID guarantees with the rationale that such guarantees are needed only by old school enterprises running monolithic, relational applications in a single private datacenter. And the premise was that modern distributed apps should instead focus on linear database scalability along with low latency, mostly-accurate, single-key-only operations on shared-nothing storage (e.g. those provided by the public clouds).

Application developers who blindly accept the above reasoning are not serving their organizations well.

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A Primer on ACID Transactions: The Basics Every Cloud App Developer Must Know

July 19, 2018

By Sid Choudhury

ACID Transactions Databases Distributed SQL How It Works

A Primer on ACID Transactions: The Basics Every Cloud App Developer Must Know

ACID transactions were a big deal when first introduced formally in the 1980s in monolithic SQL databases such as Oracle and IBM DB2. Popular distributed NoSQL databases of the past decade including Amazon DynamoDB and Apache Cassandra initially focused on “big data” use cases that did not require such guarantees and hence avoided implementing them altogether. However, ACID transactions have made a strong comeback in the last several years with the launch of next-generation distributed databases that have built-in support for them.

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A Busy Developer’s Guide to Database Storage Engines — The Basics

June 28, 2018

By Sid Choudhury

Databases Distributed SQL How It Works

A Busy Developer’s Guide to Database Storage Engines — The Basics

Editor’s note – This is the first part of a two-part series on database storage engines:

A Busy Developer’s Guide to Database Storage Engines — The Basics (this post)
A Busy Developer’s Guide to Database Storage Engines — Advanced Topics

When evaluating operational databases, developers building distributed cloud applications tend to focus on data modeling flexibility, consistency guarantees, linear scalability,

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A Busy Developer’s Guide to Database Storage Engines — Advanced Topics

June 28, 2018

By Sid Choudhury

Databases Distributed SQL How It Works

A Busy Developer’s Guide to Database Storage Engines — Advanced Topics

In the first post of this two-part series, we learned about the B-tree vs LSM approach to index management in operational databases. While the indexing algorithm plays a fundamental role in determining the type of storage engine needed, advanced considerations highlighted below are equally important to take into account.

Consistency, Transactions & Concurrency Control

Monolithic databases, which are primarily relational/SQL in nature, support strong consistency and ACID transactions.

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May 21, 2018

By Karthik Ranganathan

Community News Distributed SQL How It Works Release Announcements

YugabyteDB 1.0 — A Peek Under The Hood

Modern user-facing apps, like E-Commerce and SaaS, frequently require features from multiple databases (broadly — SQL, NoSQL and a cache) to support their multi-workload needs. App developers are responsible for understanding and managing which pieces of data should be stored in which SQL and NoSQL database. Furthermore, the app is also responsible for moving data across the tiers (e.g. populating the cache on reads and invalidating it on writes). This greatly increases development and operational complexity,

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Distributed ACID Transactions with High Performance

April 24, 2018

By Karthik Ranganathan

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

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

April 17, 2018

By Sid Choudhury

How It Works How To Kubernetes

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

March 7, 2018

By Sid Choudhury

How It Works How To MongoDB

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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Achieving Sub-ms Latencies on Large Datasets in Public Clouds

January 26, 2018

By Kannan Muthukkaruppan

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

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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Category: How It Works

Consistency, Transactions & Concurrency Control

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