Pros: schema introspection, performance, allows data modifications, full transaction supportĬons: many steps needed for setup + user management Presto Define foreign tables by specifying columns yourself or importing whole tables/schemas automatically (9.5+).Create an user mapping (so that different users could do be allowed to perform different operations on the remote tables).Overview of the steps required for setup: NB! The FDW also supports writing/changing data and transactions! Here’s the FDW full documentation. However, version 9.6 got a lot of attention in that area. True, in older Postgres versions, the plans were not always too optimal. Most importantly, size and data distribution statistics are included, so that it can figure out better execution plans. The benefit is that the local Postgres database (where the user is connected) already has the column details on the table. ![]() Basically what you have is a permanent “symlink / synonym” to a table/view on another database. You can even build complex sharding/scaling architectures on top of it, with the introduction of “foreign table inheritance” in 9.6. On board since 9.3, the Postgres foreign-data wrapper ( postgres_fdw extension, available in “contrib”) is an improvement over dblink and is well-suited for more permanent data crunching. Pros: easiest setup possible, flexibility on connecting to X amount of Postgres DBsĬons: SQLs could get ugly for multiple joins, possible performance issues for bigger datasets, basic transaction support The Postgres foreign-data wrapper One can also declare the connection directly in the dblink function itself, but then your SQL-s might get a bit unwieldy for larger number of involved databases.Increasing work_mem/temp_buffers might alleviate the IO penalty when working with bigger amounts of data pulled in from dblink.Remote data is pulled onto the server without any extra information (statistics, indexes) so if the data amounts are bigger and there are many operations on higher nodes, most probably things will be non-optimal performance wise.The query will be sent over to the specified connection, and the pulled in dataset will be handled as a normal subselect – thus from thereon one could use all the functionality that Postgres has to offer! Full documentation on the extension here. Basically you just need to create the extension (requires “contrib”), declare a named connection and then use the dblink function to specify a query, including a list of output columns and their datatypes. UnityJDBC virtual driver + SQuirrelL SQL clientĪround since ever, this method might easily be the simplest way to join independent Postgres databases.So let’s look at the following 4 options: ![]() There are also some 3rd party tools for cases when you can’t use the Postgres options (no superuser rights or extensions can be installed). Luckily PostgreSQL (plus the ecosystem) provide some options out of the box. So how do you solve such ad-hoc tasks? One could of course solve it on the application level with some simple scripting, but let’s say we only know SQL. Think sales reporting aggregations over logical clusters, or matching click-stream info, with sales orders based on customer ID’s. The goal is to present it as one logical entity. (i.e., no built-in clustering extensions are in use). BY Kaarel Moppel – With the heyday of big data and people running lots of Postgres databases, sometimes one needs to join or search for data from multiple absolutely regular and independent PostgreSQL databases.
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