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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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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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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Today, we are launching Yugabyte out of stealth and announcing the availability of YugabyteDB’s first public beta release. Yugabyte offers an open-source, cloud-native database for mission-critical applications. Yuga in Sanskrit represents an era or an epoch (about 4.32 million human years), a very long period of time. We picked the name Yugabyte to signify data that lives forever without limits. Our ultimate goal is to simplify data that is critical to businesses.
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In the world of databases today, consistency is one of the most misunderstood concepts. It is also one of the big reasons NoSQL databases are difficult to reason about.
The CAP theorem states that “in the presence of a network partition, one has to choose between consistency and availability”. In order to provide higher write availability, some NoSQL databases implement a weaker form of consistency called eventual consistency.
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Ever wondered which database Facebook (FB) uses to store the profiles of its 2.3B+ users? Is it SQL or NoSQL? How has FB database architecture evolved over the last 15+ years? As an engineer in FB database infrastructure team from 2007 to 2013, I had a front row seat in witnessing this evolution. There are invaluable lessons to be learned by better understanding the database evolution at the world’s largest social network,
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Came across the image below here and this made me smile. Not because of the implied complexity of choosing a database, but the reality with which this flow chart captures the state of the database world today in 2017. Of course, running whatever database you end up choosing in production is a whole another order of complexity.
I have been working on distributed systems for the last 10+ years.
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