Greenplum is a data management system from the big data world. It is needed by those who analyze and process dozens of terabytes of information and who are closely and uncomfortable working with conventional DBMS. Let’s talk about what kind of system it is, where and how to use it, and how it differs from other methods that work with big data.
Most Importantly: How Greenplum Works
Greenplum is based on two things:
- familiar to many PostgreSQL databases;
- the architectural concept of MPP.
More or less everything is known about PostgreSQL in Greenplum, it is often found in the work of engineers, but MPP is mentioned less often.
MPP – massively parallel processing, or massively parallel data processing. Such an architecture is quite complex under the hood, but it can be reduced to a simple conceptual description. This is an intelligent automatic breakdown of data across different servers (sharding) with an innovative automated system for executing queries on this data. This allows you to store petabytes of records and manage questions on them in a very reasonable time.
Of course, the breakdown of a large amount of data by database servers (sharding) can also be done by hand; for example, the first million records are stored on the first server, and the second on the second. The solution looks simple, but there are a lot of downsides. If all system clients need to read records from one server at once, this server may not be able to withstand it. It is also tough to scale such a system.
Greenplum takes care of all these concerns and organizes sharding on its own, taking care of all the nuances. Greenplum can also be configured with different query execution strategies based on the number of records, processors, and memory on each machine.
The system itself is not responsible for storing data; for these purposes, it uses PostgreSQL.
The combination of incredible architecture and a robust DBMS adds a powerful and performant system for those who need to deal with big data and large-scale analytics.
Who Needs Greenplum DBMS?
We have already talked about the most prominent application – such a system is indispensable when there is too much data. If 2-4 terabytes can somehow be squeezed onto one or three servers and even access this data, it is problematic to put a billion terabytes in a regular DBMS.
That is, Greenplum is needed by those who have more than a lot of data, that is, to work with big data.
In addition, storing data is part of the deal. If the records cannot be accessed in adequate time and the necessary operations can be performed, there is no sense in such data.
Therefore, Greenplum is needed by those who store vast amounts of information and actively work with them.
Of course, the problems of working with large volumes did not appear yesterday; there are tools for these tasks on the market: Click House, Cassandra, and others. But after reading the documentation, you can see that Greenplum has features that clearly define when this system is strictly needed and when it is worth choosing another despite the general scope.
Now we will talk about specific cases and differences between Greenplum and analogs.
How Greenplum Differs From Other Big Data DBMS
Greenplum supports the relational data model and preserves the immutability of the data, so it can be used for data that is sensitive to precision and structure. For example, for financial transactions. Greenplum is a good choice for banks, retail, and other companies where many transactions are carried out, and they cannot be lost.
Systems like ClickHouse Greenplum differ in scope. If Clickhouse is more suitable for statistics, Greenplum is much closer to a full-fledged DBMS with indexes and tricky queries. This allows you to access specific records quickly. In doing so, Greenplum handles analytical workloads from business intelligence to machine learning.
Greenplum also supports various types of replication and sharding, leaving all analogs far behind. This gives good performance but requires excellent tuning and many servers if you want to deploy such an on-premise system.
Also Read: Prioritizing When Choosing Data Sources
