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YugabyteDB Engineering Update – Nov 20, 2019

We are pleased to announce that YugabyteDB 2.0.5 is live!  You can read the offical release notes of this and previous versions here. This release is shipping with over 40 new enhancements and bug fixes.

What’s YugabyteDB? It is an open source, high-performance distributed SQL database built on a scalable and fault-tolerant design inspired by Google Spanner. YugabyteDB’s SQL API (YSQL) and drivers are PostgreSQL wire compatible

YSQL changes

  • Change HINT text for unsupported “alter procedure/function”.

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YugabyteDB Community Engineering Update, Tricks and Tips – Nov 8, 2019

Welcome to this week’s community update where we recap a few interesting questions that have popped up in the last week or so on the YugabyteDB Slack channel, the Forum, GitHub or Stackoverflow. We’ll also review upcoming events, new blogs and documentation that has been published since the last update. Ok, let’s dive right in:

What effect does TTL have on performance?

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YugabyteDB Engineering Update – Oct 30, 2019

We are pleased to announce that YugabyteDB 2.0.3 is live! You can read the release notes of this and previous versions here. This release is shipping with 50 new enhancements and bug fixes.

What’s YugabyteDB? It is an open source, high-performance distributed SQL database built on a scalable and fault-tolerant design inspired by Google Spanner. YugabyteDB’s SQL API (YSQL) and drivers are PostgreSQL wire compatible

[#1845] YSQL: Support for TLS Server to Server Encryption

TLS encryption is now supported between yb-master and yb-tserver processes.

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How YugabyteDB Scales to More than 1 Million Inserts Per Sec

There are a number of well-known experiments where eventually-consistent NoSQL databases were scaled out to perform millions of inserts and queries. Here, we do the same using YSQL, YugabyteDB’s PostgreSQL-compatible, strongly-consistent, distributed SQL API. We created a 100-node YugabyteDB cluster, ran single-row INSERT and SELECT workloads with high concurrency – each for an hour and measured the sustained performance (throughput and latency). This post details the results of this experiment as well as highlights the key aspects of the YugabyteDB architecture that makes it fit for such high-volume ingest workloads.

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Working with PostgreSQL Data Types in YugabyteDB

In the world of databases, data types restrict what can be considered as valid values in a table’s column. For example, if we want a column to store only integer values, we can specify that the column be an int column. Enforcing what type of data can go into a column has the added benefit of helping with storage and in some cases, query performance.

Generically, SQL data types can be broadly divided into following categories.

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9 Techniques to Build Cloud-Native, Geo-Distributed SQL Apps with Low Latency

This post is an in-depth look at the various techniques that applications needing low latency and high availability can leverage while using a geo-distributed SQL database like YugabyteDB so that the negative impacts of an high-latency, unreliable Wide Area Network (WAN) are minimized.

Geo-Distributed SQL is the Future of RDBMS

Enterprises are increasingly moving to cloud-native applications powered by microservices architecture. These applications run on elastic cloud infrastructure such as serverless frameworks and containers.

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How to: PostgreSQL Fuzzy String Matching In YugabyteDB

Before analyzing a large dataset that contains textual information, it’s important to scrub it and eliminate duplicates when necessary. To remove duplicates, you may need to compare strings referring to the same thing, but that may be written slightly different, have typos or were misspelled. Alternatively, you might need to join two tables on a column (let’s say on company name), and these can appear slightly different in both tables.

Fuzzy String Matching (or Approximate String Matching) is the process of finding strings that approximately match a pattern.

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2019 Distributed SQL Summit Recap and Highlights

Well, that’s a wrap! Yugabyte would like to extend a special thanks to JD and Amanda from the Postgreconf.org team, and to all the speakers from Facebook, Google, Amazon, Pivotal, Salesforce, Narvar, Plume Design and others that presented at the first-ever Distributed SQL Summit on Sept 20, 2019.

If you couldn’t make it out to this year’s event, have no fear!

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Getting Started with PostgreSQL Triggers in a Distributed SQL Database

Triggers are a basic feature that all monolithic SQL systems like Oracle, SQL Server and PostgreSQL have supported for many years. They are very useful in a variety of scenarios ranging from simple audit logging, to advanced tasks like updating remote databases in a federated cluster. In this blog, we’ll look at examples of INSERT, UPDATE and INSTEAD OF triggers in Yugabyte DB.

What’s Yugabyte DB? It is an open source, high-performance distributed SQL database built on a scalable and fault-tolerant design inspired by Google Spanner.

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The Effect of Isolation Levels on Distributed SQL Performance Benchmarking

This post addresses a concern raised about a benchmarking result we recently published comparing the performance of YugabyteDB, Amazon Aurora and CockroachDB. It was pointed out that we unfairly used the default isolation level for each database rather than use serializable isolation level in all databases (even though serializable level was not required for these workloads). In addition, we are also happy to share additional results with the workloads run at YugabyteDB’s serializable isolation level.

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