Why postgresql instead of mysql

If you put all such changes into a transaction block, you can make sure they all apply atomically or not at all. This drastically lowers the possibility that the database will be corrupted by a typo or other such error in the schema change, which is particularly important when you're modifying multiple related tables where a mistake might destroy the relational key. There is no way to similar way to safely adjust multiple schema sections with MySQL.

Historically, the initial PostgreSQL configuration was designed to support older flavors of UNIX where allocating large amounts of memory wasn't necessarily possible. The result was that its use of memory for caching results was, by default, very pessimistic.

On modern systems that have lots of memory available, this severely hinders untuned PostgreSQL performance. The defaults have gotten much less pessimistic in recent releases. System configuration is now examined at database initialization time and more memory allocated if it is possible to do so. As a result, the untuned default configurations on recent PostgreSQL versions perform significantly better than older versions. In addition, changes in cache management in versions 8.

The primary tunable for both database systems works similarly, by allocating a block of shared memory dedicated to the database.

It is still worthwhile to go through the configuration files to tune them to match the available memory on a database server, as all these defaults are dramatically undersized compared to the amount of RAM in current systems. Performance comparisons using the defaults with either database are completely unrealistic of how a real system would be configured. Benchmarks are very difficult to do well; creating truly comparable benchmarks is a complex art. There are just enough hardware differences between the two systems that it isn't fair to directly compare the two results.

But the fact that both scores are close to one another and the configuration is similar does suggest that while there may be performance differences between the two database systems, the magnitude of that difference is not particularly large with this application type. For comparison sake, an Oracle on HP result offers a similar magnitude of performance on less impressive hardware, suggesting both open-source databases still lag the best of the proprietary products in absolute performance efficiency.

Some suggest Oracle's lead is even larger if you pick examples to put it in a better light, but be sure to read Benchmark Brou-Ha-Ha for some comments on actual pricing here and to pick up some comments on a second PostgreSQL result using a smaller server. If you do a fair comparison that includes software licensing costs, the performance per dollar figures for both PostgreSQL and MySQL are both similar to one another and very good relative to the average for the database industry.

It would however be untrue to say that these open-source solutions are always a better choice than proprietary offerings like Oracle just based on that; certainly the feature sets and absolute performance of each solution need to be considered as well. A short summary of the main benefit of MVCC would be "readers are never blocked by writers".

MVCC is used to implement a pessimistic implementation of the four SQL standard transaction isolation levels: "when you select the level Read Uncommitted you really get Read Committed, and when you select Repeatable Read you really get Serializable, so the actual isolation level may be stricter than what you select.

InnoDB databases supports all four SQL standard transaction isolation levels , with the default being "repeatable read".

When comparing the two models, PostgreSQL enforces client separation where the data operated on is always consistent under all circumstances; as the MVCC documentation states, "the reason that PostgreSQL only provides two isolation levels is that this is the only sensible way to map the standard isolation levels to the multiversion concurrency control architecture.

However, in situations where it's acceptable for data being read to have small inconsistencies, being able to use a less strict locking could be a performance advantage for MySQL. Even when both systems are configured to one of the strict levels of transaction locking, the differences between the two implementations are subtle enough that which implementation will work better for a particular application is hard to state definitively.

Partially because the PostgreSQL locking implementation is very mature it's always active and performance of the associated code is accordingly critical , even in situations where MySQL initially appears faster PostgreSQL can pull ahead and scale to higher throughput when the number of simultaneous users becomes large. A good example of such a situation is demonstrated in the tweakers. This normally results in a sequential scan reading information about every row in the table. It is necessary to actually examine the rows themselves to determine if they are visible to the transaction or not.

That is the reason why there exists so much MySQL code that uses this construct assuming it's a trivial operation. But if you're using InnoDB instead, this is no longer the case. It is worth observing that it is only this precise form of aggregate that must be so pessimistic; if augmented with a "WHERE" clause like. You can replicate all databases, selected databases or even selected tables within a database. MySQL Cluster is a technology providing shared-nothing no single point of failure clustering and auto-sharding partitioning for the MySQL database management system.

Internally MySQL Cluster uses synchronous replication through a two-phase commit mechanism to guarantee that data is written to multiple nodes. Here's a full list of the contributors , and the source code on Github. Along with a wealth of online documentation , PostgreSQL has a wide variety of community support, including mailing lists and IRC as well as third-party commercial support options. For proprietary use, several paid editions are available and offer additional functionality.

Try it free today! Topics Topics. Speak with a Panoply Data Architect. Resources Resources. Integrations Integrations. Why Panoply. Demo Demo. Visit Panoply. By Ben Rogojan February 15, Who uses these databases? Get a free consultation with a data architect to see how to build a data warehouse in minutes.

Read more in:. Databases Mysql Postgres. Share this post:. Also Check Out Data warehouses are becoming increasingly popular as many businesses, nonprofits, and municipal organizations realize the value of storing their current and historic So you're at the point of your project where the discussion of SQL vs.

This is especially important if you plan to extend your operations in the future. We can break down their support multiple tiers of support options into several categories:. Additionally, MySQL has more StackOverflow questions and answers, as well as a wider range of materials books, documentation to help you resolve your issues.

In that respect, both languages are capable of analyzing a wide constellation of data and answering business questions across the board. However, Postgres has developed more advanced features for analytics.

These speed up and optimize analytical queries:. If your use case involves analytics - or is likely to involve more complex analytical queries in the foreseeable future - choose Postgres.

Additionally, if you expect to be dealing with a lot of data cleaning in your pipelines, Postgres offers more out-of-the-box solutions for wrangling messy data. With its variety of analytic functions, it will speed up and optimize your analytical operations. This makes PostgreSQL an excellent choice for your data science and big data needs. NoSQL refers to data modeling, which does not follow the tabular Excel-like form of relational databases.

The challenge of modeling and storing NoSQL data is relatively new. Despite this, the demand for NoSQL databases is increasing due to the proliferation of non-structured data social networks, which are graphs, Internet of Things logs, XML as a representation of web pages, geospatial data coordinates for geolocating specific events, etc.

Postgres and MySQL treat timestamps very differently. On the other hand, Postgres offers the same functionality but also has the option to save timestamps with timezone as a native data type. For the majority of use cases, users do not need to concern themselves with knowing when an event occurred and in which timezone. But for time-sensitive operations such as financial trading, digital advertising attributions, certain IoT applications, etc.

If you do work with time series, MySQL is simply not the right choice. Opt for Postgres instead. ACID is a standard set of properties for low-level operations, which guarantee that database transactions perform without errors or corruption. That is, create, read, update, or delete CRUD records. What could go wrong? The non-ACID nightmare : Imagine your customer buys from your e-shop while at the same time, your data engineer runs an update on the database and migrates tables to a different location.

Because the database did not comply with ACID standards, only part of the purchase details were written into the orders table before it was migrated.

How will you resolve that? And how will you even know which products are missing? In some use cases, you need to have exhaustive data quality.

For these cases, make sure to pick a database storage engine that is ACID compliant. Both MySQL and Postgres are fantastic examples of relational database management systems, but with different comparative strengths. MySQL provides better support and is accompanied by a greater number of developers who know its workings inside out.