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
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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This post highlights the architectural considerations that went into the design of the DocDB and YQL layers for the YugabyteDB database.
Today, we are launching Yugabyte out of stealth and announcing the availability of YugabyteDB’s first public beta release.
Dive into the types of consistency referenced by the CAP theorem, learn how they impact an app, and get the facts re: common myths about eventual consistency.
