The YugaByte Database Blog

Thoughts on open source, cloud native and distributed databases

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

In the first post of this series, we learnt 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,

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YugaByte DB 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.

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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 DBs, distributed NoSQL/non-relational DBs either forsake them completely or support only a highly restrictive single-key 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 YugaByte DB

A few of our early users have chosen to build their new cloud applications on YugaByte DB 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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Achieving Sub-ms Latencies on Large Datasets in Public Clouds

One of our users was interested to learn more about YugaByte DB’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 YugaByte DB was designed well to handle this case with the optimal number of IOs to the disk subsystem.

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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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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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