By Hans-Jurgen Schonig
Leverage the ability of PostgreSQL replication to make your databases extra strong, safe, scalable, and fast
About This Book
- Efficiently mirror PostgreSQL utilizing high-end strategies to guard your facts and run your server with out interruptions
- Improve reliability, fault-tolerance, and accessibility by means of retaining consistency among redundant resources
- Get grips with the most recent model of PostgreSQL with this example-based guide
Who This publication Is For
This e-book is perfect for PostgreSQL directors who are looking to arrange and comprehend replication. by way of the top of the ebook, it is possible for you to to make your databases extra powerful and safe by means of attending to grips with PostgreSQL replication.
What you'll Learn
- Use Point-in-time restoration to accomplish info restoration in addition to replication
- Set up synchronous in addition to asynchronous streaming replication
- Get familiarized with the transaction log, the middle component to so much replication setups and its purpose
- Improve velocity and reliability with an knowing of pgpool and PgBouncer
- Increase your information safeguard and geographically distribute data
- Make your structures extra on hand and safe with Linux excessive Availability
- Scale out with PL/Proxy and Postgres-XC
- Detect, examine, and clear up replication-related problems
In Detail
PostgreSQL deals a accomplished set of replication similar gains. Unleashing the facility of PostgreSQL provide you with numerous possibilities and a aggressive virtue over different database systems.
This ebook will advisor you thru an important innovations of PostgreSQL replication. It comprises the entire info you must layout and function replicated setups.
Beginning via supplying you with an knowing of replication recommendations, the PostgreSQL transaction log, and Point-in-time restoration, we progressively stream directly to establishing asynchronous and synchronous replication. subsequent up, you'll learn how to computer screen a PostgreSQL cluster setup, care for tracking instruments, after which circulation directly to realizing Linux excessive Availability. extra, we discover widely-used instruments resembling Slony, SkyTools, Postgres-XC, and walbouncer, and organize PL/Proxy.
Finally, you will get familiar with the recent know-how of BDR, which permits bidirectional replication in PostgreSQL.
Read or Download PostgreSQL Replication PDF
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Additional info for PostgreSQL Replication
Example text
Range partitioning means that each server has a fixed slice of data for a given time frame. This can be beneficial if you want to perform time series analysis or something similar. However, it can be counterproductive if you want to make sure that data is split evenly. If you expect your cluster to grow, we recommend starting with more partitions than those initially necessary, and packing more than just one partition on a single server. Later on, it will be easy to move single partitions to additional hardware joining the cluster setup.
Let's create a table and see what happens: test=# CREATE TABLE t_test (id int4); CREATE TABLE We can check the system table now to retrieve the so-called relfilenode variable, which represents the name of the storage file on the disk: test=# SELECT relfilenode, relname FROM WHERE pg_class relname = 't_test'; relfilenode | relname -------------+--------16385 | t_test (1 row) Note that relfilenode can change if TRUNCATE or similar commands occur on a certain table. As soon as the table is created, PostgreSQL will create an empty file on the disk: [hs@paula base]$ ls -l 16384/16385* -rw------- 1 hs staff 0 Feb 12 12:06 16384/16385 Growing data files Tables can sometimes be quite large, and therefore it is not wise to put all of the data related to a table into a single data file.
Instead of just doubling your cluster (which is fine for most cases), you can also give more thought to writing a more sophisticated partitioning function that leaves the old data in place but handles the more recent data more intelligently. Having time-dependent partitioning functions might cause issues of its own, but it might be worth investigating this path. Some NoSQL systems use range partitioning to spread out data. Range partitioning means that each server has a fixed slice of data for a given time frame.
