Lots has happened since our last engineering update about 3 months ago. Below are some of the highlights.
We have made a lot of progress on YSQL, the PostgreSQL compatible distributed SQL API for YugabyteDB! You can also read about YSQL architecture which covers how distributed SQL is implemented in YugabyteDB.
We were at the first ever PostgresConf Silicon Valley in October 2018.
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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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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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This is the first post in a series about building real-world, distributed cloud services using a transactional cloud database like YugabyteDB.
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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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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YugabyteDB 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.
When using your Redis application with YugabyteDB, your data is replicated and persisted with strong consistency.
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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 YugabyteDB 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.
The graph above shows how you can achieve linear scalability with YugabyteDB.
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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:
In general time series data has the following characteristics:
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YugabyteDB is a transactional, high performance, geo-distributed operational database that converges multiple NoSQL and SQL interfaces into an unified solution. The v0.9 public beta of YugabyteDB includes the YCQL and YEDIS APIs that are compatible with Cassandra Query Language (CQL) and Redis APIs respectively. PostgreSQL-compatible YSQL API is under development. A fundamental design goal for YugabyteDB has been to provide the same transactional, performance and operational simplicity guarantees irrespective of the API used.
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