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Demystifying JSON with CockroachDB… Import, Index, and Computed Columns

Overview

Recently, I created and delivered an "Advanced Developer Workshop" for CockroachLabs. One of the topics dove into how to ingest and use JSON data.

Like many databases, CockroachDB has the ability to use JSON data type for columns within a table. Basically, you insert a JSONB object into a row, and then can filter and extract the desired data with SQL. The following simple example shows how this is done:

So it is pretty straight forward to use JSONB objects within tables, but how do you load those HUGE json files into CockroachDB?

How SQL Server MVCC compares to Oracle and PostgreSQL

By Franck Pachot

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Microsoft SQL Server has implemented MVCC in 2005, which has been proven to be the best approach for transaction isolation (the I in ACID) in OLTP. But are you sure that writers do not block readers with READ_COMMITTED_SNAPSHOT? I’ll show here that some reads are still blocked by locked rows, contrary to the precursors of MVCC like PostgreSQL and Oracle.

For this demo, I run SQL Server 2019 RHEL image on docker in an Oracle Cloud compute running OEL7.7 as explained in the previous post. If you don’t have the memory limit mentioned, you can simply run:

docker run -d -e "ACCEPT_EULA=Y" -e 'MSSQL_PID=Express' -p 1433:1433 -e 'SA_PASSWORD=**P455w0rd**' --name mssql mcr.microsoft.com/mssql/rhel/server:2019-latest

How SQL Server MVCC compares to Oracle and PostgreSQL

By Franck Pachot

.
Microsoft SQL Server has implemented MVCC in 2005, which has been proven to be the best approach for transaction isolation (the I in ACID) in OLTP. But are you sure that writers do not block readers with READ_COMMITTED_SNAPSHOT? I’ll show here that some reads are still blocked by locked rows, contrary to the precursors of MVCC like PostgreSQL and Oracle.

For this demo, I run SQL Server 2019 RHEL image on docker in an Oracle Cloud compute running OEL7.7 as explained in the previous post. If you don’t have the memory limit mentioned, you can simply run:

docker run -d -e "ACCEPT_EULA=Y" -e 'MSSQL_PID=Express' -p 1433:1433 -e 'SA_PASSWORD=**P455w0rd**' --name mssql mcr.microsoft.com/mssql/rhel/server:2019-latest

Oracle and postgres disk IO performance

This post is about one of the fundamentally important properties of a database: how IO is done. The test case I studied is doing a simple full table scan of a single large table. In both Oracle and postgres the table doesn’t have any indexes or constraints, which is not a realistic example, but this doesn’t change the principal topic of the study: doing a table scan.

I used a publicly available dataset from the US bureau of transportation statistics called FAF4.5.1_database.zip
The zipped file is 347MB, unzipped size 1.7GB.

Importing geo-partitioned data… the easy way

 

setting the stage

I started at Cockroach labs back in June 2019 to help others learn how to architect and develop applications using a geo-distributed database.  There has been a resurgence in distributed database technology, but the focus on geo-distributed is quite unique to CockroachDB.  While the underlying technology is unique, developers and DBAs that come with a wealth of experience, need to know how to best use this innovative technology.  Given this situation, I thought it would be good to start a blog series to explore various topics facing anyone beginning to architect database solutions with CockroachDB.

To start using a database, the first step is to IMPORT table data so you can begin to see how the database performs and responds.  And thus the IMPORT series has started!

Seattle PostgreSQL Meetup This Thursday: New Location

I’m looking forward to the Seattle PostgreSQL User Group meetup this Thursday (June 20, 2019) at 5:30pm! We’re going to get an early sneak peek at what’s coming later this year in PostgreSQL’s next major release. The current velocity of development in this open source community is staggering and this is an exciting and valuable opportunity to keep up with where PostgreSQL is going next.

One thing that’s a bit unusual about this meetup is the new location and late timing of the announcement. I think it’s worth a quick blog post to mention the location: for some people this new location might be a little more accessible than the normal spot (over at the Fred Hutch).

Seattle PostgreSQL Meetup This Thursday: New Location

I’m looking forward to the Seattle PostgreSQL User Group meetup this Thursday (June 20, 2019) at 5:30pm! We’re going to get an early sneak peek at what’s coming later this year in PostgreSQL’s next major release. The current velocity of development in this open source community is staggering and this is an exciting and valuable opportunity to keep up with where PostgreSQL is going next.

One thing that’s a bit unusual about this meetup is the new location and late timing of the announcement. I think it’s worth a quick blog post to mention the location: for some people this new location might be a little more accessible than the normal spot (over at the Fred Hutch).

Seattle PostgreSQL Meetup This Thursday: New Location

I’m looking forward to the Seattle PostgreSQL User Group meetup this Thursday (June 20, 2019) at 5:30pm! We’re going to get an early sneak peek at what’s coming later this year in PostgreSQL’s next major release. The current velocity of development in this open source community is staggering and this is an exciting and valuable opportunity to keep up with where PostgreSQL is going next.

One thing that’s a bit unusual about this meetup is the new location and late timing of the announcement. I think it’s worth a quick blog post to mention the location: for some people this new location might be a little more accessible than the normal spot (over at the Fred Hutch).

Do you know what you are measuring with pgbench?

pgbench flamegraph

pgbench is a benchmark application for PostgreSQL. You define some parameters for the workload (read-only, volume of data, number of threads, cursor sharing, …) and measure the number of transactions per second. Pgbench is used a lot when one wants to compare two alternative environments, like different postgres version, different platform, different table design,…

However, a scientific approach should go beyond the simple correlation between the observed performance (transactions per seconds) and the configuration. Without a clear analysis and explanation on the cause-consequence, we cannot extrapolate from a single set of observations to a general recommendation. The goal of this post is to show what is behind this ‘transaction per second’ measure.