In case you missed the news earlier this Summer, YugaByte raised an additional $16M of funding from Dell Technologies Capital and our previous investor Lightspeed Venture Partners. With the additional funding, we are accelerating investments in engineering, sales, and customer success to scale our support for enterprises building business-critical applications in the cloud. So, as you’d expect…
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After billions of dollars in capital expenditure and reference customers in every major vertical, Google Cloud Platform has finally emerged as a credible competitor to Amazon Web Services and Microsoft Azure when it comes to enterprise-ready cloud infrastructure. While Google Cloud’s compute and storage offerings are easier to understand, making sense of its various managed database offerings is not for the faint-hearted. This post introduces app developers to the major Google Cloud database services,
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Our post 6 Signs You Might be Misunderstanding ACID Transactions in Distributed Databases describes the key challenges involved in building high performance distributed transactions. Multiple open source ACID-compliant distributed databases have started building such transactions by taking inspiration from research papers published by Google. In this post, we dive deeper into Percolator and Spanner, the two Google systems behind those papers, as well as the open source databases they have inspired.
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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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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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