Thoughts on distributed databases, open source and cloud native
After launching YugaByte DB in November 2017, Team YugaByte celebrated 2018 as its first full year in the market as a cloud native, transactional database company. Exhilarating is the one word that best summarizes our 2018 experience. From a product and engineering standpoint, we launched two major releases (and tens of minor releases) and saw users adopt each of the releases at an amazing pace.
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Read MoreThe NoSQL database revolution started with the publication of the Google BigTable and Amazon Dynamo papers in 2006 and 2007 respectively. These original designs focused on horizontal write scalability without compromising the performance observed in the single node databases dominant at that time. The compromises instead came either in the form of eventual consistency (i.e. inability to read the last update) or loss of multi-key access patterns (such as SQL integrity/foreign key constraints,
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YugaByte is excited to be at KubeCon today to announce Kubernetes StatefulSets support for our distributed SQL API which complements the transactional NoSQL APIs already generally available. YSQL is YugaByte DB’s PostgreSQL-compatible Distributed SQL API (currently in Beta). This new feature, available in YugaByte DB 1.1.7, cloud-native applications and microservices can rely on SQL and NoSQL to take full advantage of Kubernetes StatefulSets to power horizontally scalable,
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Read MoreTeam YugaByte was at AWS re:Invent in Las Vegas last week. While AWS was announcing a flurry of new product releases and existing product updates, we had some excellent deep dive conversations at our booth on the future of transactional databases and how YugaByte DB is playing its part in shaping that future. This post summarizes our key learnings from the conference,
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Read MoreLots 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 YugaByte DB! You can also read about YSQL architecture which covers how distributed SQL is implemented in YugaByte DB.
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Read MoreApplication developers usually spend considerable time evaluating multiple operational databases to find that one database that’s best fit for their workload needs. These needs include simplified data modeling, transactional guarantees, read/write performance, horizontal scaling and fault tolerance. Traditionally, this selection starts out with the SQL vs. NoSQL database categories because each category presents a clear set of trade-offs. High performance in terms of low latency and high throughput is usually treated as a non-compromisable requirement and hence is expected in any database chosen.
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Read MoreOur post Getting Started with Distributed Backups in YugaByte DB details the core architecture powering distributed backups in YugaByte DB. It also highlights a few backup/restore operations in a single region, multi-AZ cluster. In this post, we perform distributed backups in a multi-region YugaByte DB cluster and verify that we achieve performance characteristics similar to those observed in a single region cluster.
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Read MoreYugaByte DB is a distributed database with a Google Spanner-inspired strongly consistent replication architecture that is purpose-built for high availability and high performance. This architecture allows administrators to place replicas in independent fault domains, which can be either availability zones or racks in a single region or different regions altogether. These types of multi-AZ or multi-region deployments have the immediate advantage of guaranteeing organizations a higher order of resilience in the event of a zone or region failure.
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Read MorePresto is a distributed SQL query engine optimized for OLAP queries at interactive speed. It was created by Facebook and open-sourced in 2012. Since then, it has gained widespread adoption and become a tool of choice for interactive analytics. It supports standard ANSI SQL, including complex queries, aggregations, joins, and window functions. It has a connector architecture to query data from many data sources such as SQL and NoSQL databases as well as traditional big data platforms such as Hive/Hadoop.
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Read MoreWeb app developers initially adopted MongoDB for its ability to model data as “schemaless” JSON documents. This was a welcome relief to many who were previously bitten by the rigid structure and schema constraints of relational databases. However, two critical concerns that have been a thorn on MongoDB’s side over the years are that of data durability and ACID transactions.
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