What is MySQL?
What is PostgreSQL?
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What tools integrate with MySQL?
What tools integrate with PostgreSQL?
In 2014, Uber began working on Big Data Generation 1. Prior to 2014, all of the company’s data could fit into “a few traditional online transaction processing (OLTP) databases (in [their] case, MySQL and PostgreSQL ).” At that time, engineers were required to write individual code to access and join data across databases, a take made more difficult by the fact that there was no global view of all stored data, which was scattered around several OLTP databases with a “total data size on the order of a few terabytes.”
They chose Vertica as the analytical data warehouse, because of “because of its fast, scalable, and column-oriented design. We also developed multiple ad hoc ETL (Extract, Transform, and Load) jobs that copied data from different sources (i.e. AWS S3, OLTP databases, service logs, etc.) into Vertica.
Uber's early architecture was a monolithic Python backend on top of Postgres. Between 2014 and 2016, Uber moved many applications off of Postgres and onto MySQL, accessed through a sharding layer built in-house called Schemaless.
As the company scaled, Uber encountered issues with scaling writes due to write amplification issues (1 logical write causing 4 physical writes) and inefficient data replication with Postgres. Postgres replication works at the physical level, which means that it's not possible to replicate data between data bases running different versions. This added complexity and risk to upgrades.
MySQL, and the InnoDB storage engine in particular, can do replication at the statement level, which poses less risks and works between versions. Uber also found MySQL's caching and connection pooling capabilities to provide superior performance for their use cases.
In my opinion PostgreSQL is totally over MongoDB - not only works with structured data & SQL & strict types, but also has excellent support for unstructured data as separate data type (you can store arbitrary JSONs - and they may be also queryable, depending on one of format's you may choose). Both writes & reads are much faster, then in Mongo. So you can get best on Document NoSQL & SQL in single database..
Formal downside of PostgreSQL is clustering scalability. There's not simple way to build distributed a cluster. However, two points:
1) You will need much more time before you need to actually scale due to PG's efficiency. And if you follow database-per-service pattern, maybe you won't need ever, cause dealing few billion records on single machine is an option for PG.
2) When you need to - you do it in a way you need, including as a part of app's logic (e.g. sharding by key, or PG-based clustering solution with strict model), scalability will be very transparent, much more obvious than Mongo's "cluster just works (but then fails)" replication.
We use postgresql for the merge between sql/nosql. A lot of our data is unstructured JSON, or JSON that is currently in flux due to some MVP/interation processes that are going on. PostgreSQL gives the capability to do this.
At the moment PostgreSQL on amazon is only at 9.5 which is one minor version down from support for document fragment updates which is something that we are waiting for. However, that may be some ways away.
Other than that, we are using PostgreSQL as our main SQL store as a replacement for all the MSSQL databases that we have. Not only does it have great support through RDS (small ops team), but it also has some great ways for us to migrate off RDS to managed EC2 instances down the line if we need to.
PostgreSQL combines the best aspects of traditional SQL databases such as reliability, consistent performance, transactions, querying power, etc. with the flexibility of schemaless noSQL systems that are all the rage these days. Through the powerful JSON column types and indexes, you can now have your cake and eat it too! PostgreSQL may seem a bit arcane and old fashioned at first, but the developers have clearly shown that they understand databases and the storage trends better than almost anyone else. It definitely deserves to be part of everyone's toolbox; when you find yourself needing rock solid performance, operational simplicity and reliability, reach for PostgresQL.
Relational data stores solve a lot of problems reasonably well. Postgres has some data types that are really handy such as spatial, json, and a plethora of useful dates and integers. It has good availability of indexing solutions, and is well-supported for both custom modifications as well as hosting options (I like Amazon's Postgres for RDS). I use HoneySQL for Clojure as a composable AST that translates reliably to SQL. I typically use JDBC on Clojure, usually via org.clojure/java.jdbc.
We are used MySQL database to build the Online Food Ordering System
- Its best support normalization and all joins ( Restaurant details & Ordering, customer management, food menu, order transaction & food delivery).
- Best for performance and structured the data.
- Its help to stored the instant updates received from food delivery app ( update the real-time driver GPS location).
1.It's very popular. Heared about it in Database class 2. The most comprehensive set of advanced features, management tools and technical support to achieve the highest levels of MySQL scalability, security, reliability, and uptime. 3. MySQL is an open-source relational database management system. Its name is a combination of "My", the name of co-founder Michael Widenius's daughter, and "SQL", the abbreviation for Structured Query Language.
We use MySQL and variants thereof to store the data for our projects such as the community. MySQL being a well established product means that support is available whenever it is required along with an extensive list of support articles all over the web for diagnosing issues. Variants are also used where needed when, for example, better performance is needed.
PostgreSQL is responsible for nearly all data storage, validation and integrity. We leverage constraints, functions and custom extensions to ensure we have only one source of truth for our data access rules and that those rules live as close to the data as possible. Call us crazy, but ORMs only lead to ruin and despair.
MySQL is a freely available open source Relational Database Management System (RDBMS) that uses Structured Query Language (SQL). SQL is the most popular language for adding, accessing and managing content in a database. It is most noted for its quick processing, proven reliability, ease and flexibility of use.
Tried MongoDB - early euphoria - later dread. Tried MySQL - not bad at all. Found PostgreSQL - will never go back. So much support for this it should be your first choice. Simple local (free) installation, and one-click setup in Heroku - lots of options in terms of pricing/performance combinations.
I am not using this DB for blog posts or data stored on the site. I am using to track IP addresses and fully qualified domain names of attacker machines that either posted spam on my website, pig flooded me, or had more that a certain number of failed SSH attempts.
Used during the "build process" of Coolfront Mobile's Flat rate search engine database. Flat rate data that resides in Salesforce is transformed using SQLite into a format that is usable for our mobile Flat rate search engine (AKA: Charlie).
RDBTools is a self-hosted application, and it is important that the installation process is simple. With SQLite, we create a new database file for every analysis. Once the analysis is done, the SQLite file can be thrown away easily.
All the dynamic data (i.e.: jobs) is stored in a simple SQLite database.
Все динамические данные (вакансии) хранятся в простой SQLite БД.