![]() We need around 81 seconds to run this simple test, which is A LOT of time. Running the script can be done using psql: INSERT INTO t_sample VALUES ('abcd', 1, 'abcd', 1) In the next step, we will compile a script containing 1 million INSERT statements in a single transaction: The sample table consists of 4 columns which is pretty simple. Let’s create a table as well as some sample data: To show what kind of impact this change has in terms of performance, I have compiled a short example. Whenever you want to write large amounts of data, data COPY is usually the way to go. In the case of COPY, this is only done once, which is a lot faster. People often ask: What kind of overhead is there? What makes COPY so much faster than INSERT? There are a variety of reasons: In the case of INSERT, every statement has to check for locks, check for the existence of the table and the columns in the table, check for permissions, look up data types and so on. The reason is that INSERT has a lot of overhead. The first thing to consider is that COPY is usually a LOT better than plain inserts. Let us take a look at these things in greater detail. Improving column order and space consumption.There are several things to take into consideration in order to speed up bulk loading of massive amounts of data using PostgreSQL: You can use this knowledge to optimize data warehousing or any other data-intensive workload. This post will show you how to use some of these tricks, and explain how fast importing works. There are various ways to facilitate large-scale imports, and many different ways to scale are also available. Bulk loading pg_bulkload PGLoader postgresql unlogged zheapīulk loading is the quickest way to import large amounts of data into a PostgreSQL database.
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