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.
Modern user-facing applications are increasingly moving to a multi-region,
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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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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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MongoDB’s sharding and replication have improved since it was first an AP database, but it still has limitations for highly scalable and latency-sensitive apps. In this blog, the authors discuss why users switch to YugabyteDB due to distributed ACID transactions and improved scalability.
Explore building a chat app with YugabyteDB, a transactional cloud database. This series begins with a scalable Facebook Messages-like application, leveraging YugabyteDB’s capabilities to handle complex chat functionalities efficiently and effectively. Stay tuned for detailed insights and practical examples in our upcoming posts.
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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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. We also highlight the lesser-known-but-equally-important consistency-latency tradeoff imposed by the PACELC Theorem that extends CAP to normal operations.
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YugabyteDB’s compatibility with the Redis API enhances data replication and consistency, offering a ‘unified cache + database’ solution. It simplifies data sharding, supports large datasets without full memory reliance, and provides polyglot persistence for diverse workloads.
Yugabyte’s 50-node cluster benchmark demonstrates linear scalability in YugabyteDB, doubling throughput from 25 to 50 nodes with consistently low latencies. Conducted on Google Cloud Platform, these results highlight YugabyteDB’s cloud-native capabilities, applicable across various cloud and on-premise environments.
This blog highlights the challenges of handling time series data with vanilla Redis and the efficiency of YugaByte’s TS datatype