Five shades of sharding
The importance of sharding is now colossal. The size of today's databases exceeds 100 terabytes, scaling up vertically, and adding replicas containing a complete physical copy of the database is becoming difficult, especially when computational resources are scarce. Database sharding is a way to scale horizontally by partitioning data between compute nodes that are independent of each other.
In the PostgreSQL world, there are well-known scaling tools - CitusDB, and Greenplum - as well as new generation solutions - Cockroach DB, Yugabyte DB, SPQR, and Shardman.
In this talk, we will discuss the difference between these implementations, the advantages, and disadvantages of these solutions, examine the current state of implementation of sharding in vanilla PostgreSQL, and also touch on another important topic - providing data integrity and consistency guarantees across a distributed cluster.
Слайды
Видео
Видео доступно участникам мероприятия, выполнившим вход в личный кабинет
Другие доклады
-
45 мин
Vladimir Lipunov ГАИШ МГУExtreme Astronomy
A popular lecture with pictures illustrating the most powerful phenomena in the Universe that put space and time in an uncomfortable position and force them to reveal the secrets of the Universe to those who have an inquisitive mind and Postgres! The story of Mr. Lipunov, professor at Moscow State University, author of world-famous popular and scientific books, theorist, experimenter and creator of a network of astronomical robots.
-
45 мин
Василий Тимощенко ПКТБ-ЦЦТ ОАОExperience of migration a highly loaded system from DB2 for z/OS to PostgresPro in multimaster configuration
The developer shares his experience of migration a highly loaded system in the field of railway transport from IBM DB2 for z/OS to PostgresPro in a multimaster configuration. The main focus is on the nuances of working with a cluster operating in multimaster mode, what problems had to be faced and how to solve them.
-
22 мин
Pavel Tolmachev Postgres ProfessionalLet's get acquainted with GEQO in 20 minutes
-----------------------------------------------------------QUERY PLAN-------------------------------------------------------------- Hash Join Hash Cond: (Subject = GEQO) -> Hash Join Hash Cond: (**Optimizer task = choose the best query execution plan**) -> Seq Scan on **The number of potential plans grows exponentially as the number of tables in a query increases** -> Hash -> Seq Scan on **PostgreSQL solves this problem by using the genetic optimizer (GEQO)** -> Hash -> Seq Scan on **Topics of the report:** Filter: (**(What is GEQO)** AND **(Pros and cons)** AND **(How it works)**) (10 rows)
-
45 мин
Alena Rybakina Postgres Professional