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Sometimes, PostgreSQL databases need to import large quantities of data in a single or a minimal number of steps. This process can be sometimes unacceptably slow. In this article, we will cover some best practice tips for bulk importing data into PostgreSQL databases.
Vacuum and Analyze are the two most important PostgreSQL database maintenance operations. Although they sound relatively straightforward, DBAs are often confused about running these processes manually or setting the optimal values for their configuration parameters. In this article, we will share a few best practices for VACUUM and ANALYZE.
Sometimes, PostgreSQL databases need to import large quantities of data in a single or a minimal number of steps. This is commonly known as bulk data import where the data source is typically one or more large files. This process can be sometimes unacceptably slow. In this article, we will cover some best practice tips for bulk importing data into PostgreSQL databases. However, there may be situations where none of these tips will be an efficient solution. We recommend readers consider the pros and cons of any method before applying it.
In this article, I will cover some fundamental practices to get the best out of PostgreSQL logs. This blog is not a hard and fast rule book; readers are more than welcome to share their thoughts in the comments section. To get the best value out of it though, I ask the reader to think about how they want to use their PostgreSQL database server logs:
PostgreSQL 13 was released last week. As a PostgreSQL developer, of course I monitor the news and social media on days like this to see what the public thinks about our release and maybe which features get discussed most. The latter is always surprising. What I noticed particularly this year was that most of the […]
If you are enjoying working with PostgreSQL declarative partitioning, you might be wondering how to check which partition contains a specific record. While it is quite obvious in the cases of list or range partitioning, it is a bit trickier with hash partitioning. Don’t worry. Here you can find a quick way to determine which […]
As part of the ongoing PostgreSQL Webinar Series, 2ndQuadrant hosted a webinar on Business Intelligence with Window Functions in PostgreSQL, which gave an overview of Window Functions, how to use them in PostgreSQL, along with discussing specific features and detailed examples.
With the release yesterday of PostgreSQL 13, now is perhaps a good time to talk about when and how it should be deployed. We often get questions at such times like "When should I upgrade?" and "Should I switch my new planned deployment to the new release?" The first thing to consider is this: you […]
This post looks at full-text search, i.e. the ability to index and search in large amounts of text data. The same infrastructure (especially the indexes) may be useful for indexing semi-structured data like JSONB documents etc. but that’s not what this benchmark is focused on.
This post continues from my report on Random Numbers. I have begun working on a random data generator so I want to run some tests to see whether different random number generators actually impact the overall performance of a data generator. Let’s say we want to create random data for a table with 17 columns, […]
In this article, I will cover some fundamental practices to get the best out of PostgreSQL logs. This blog is not a hard and fast rule book; readers are more than welcome to share their thoughts in the comments section. To get the best value out of it though, I ask the reader to think about how they want to use their PostgreSQL database server logs:
A couple years ago (at the pgconf.eu 2014 in Madrid) I presented a talk called “Performance Archaeology” which showed how performance changed in recent PostgreSQL releases. I did that talk as I think the long-term view is interesting and may give us insights that may be very valuable. For people who actually work on PostgreSQL […]
Sometimes, PostgreSQL databases need to import large quantities of data in a single or a minimal number of steps. This is commonly known as bulk data import where the data source is typically one or more large files. This process can be sometimes unacceptably slow. In this article, we will cover some best practice tips for bulk importing data into PostgreSQL databases. However, there may be situations where none of these tips will be an efficient solution. We recommend readers consider the pros and cons of any method before applying it.
In the first part of this blog series, I’ve presented a couple of benchmark results showing how PostgreSQL OLTP performance changed since 8.3, released in 2008. In this part I plan to do the same thing but for analytical / BI queries, processing large amounts of data. There’s a number of industry benchmarks for testing […]
I’ve been slowly working on developing open source database systems performance testing tools from scratch. One of the components of this toolset is a data generator that can build a dataset to be loaded into a database. I’ll need a random number generator for that and these are the requirements that I think are most […]
This webinar gave an overview of PostgreSQL partitioning and how it plays a key role in sensible management and improved performance with very large databases. Particular use cases were also highlighted, along with taking a look at partitioning improvements in open source PostgreSQL and what has been done to make PostgreSQL's partitioning feature even better in the future.
Postgres BDR has seen exciting growth in the financial services and telecommunication industries. These industries present a new set of challenges that push the limits of Postgres Synchronous COMMIT features for Highly Available clusters. This webinar explored the Postgres Synchronous COMMIT deployment models and discussed the limitations associated with them. The webinar also introduced a […]
This webinar gave a comprehensive overview of how to perform a “near” zero downtime upgrade using pglogical, but also covered small things which, if not taken care of in time, can end up in an extended upgrade, or worse, in a failed one.