Alternatives to Patroni logo

Alternatives to Patroni

Citus, PostgreSQL, Slick, Spring Data, and Microsoft SQL Server Management Studio are the most popular alternatives and competitors to Patroni.
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What is Patroni and what are its top alternatives?

Patroni is a template for you to create your own customized, high-availability solution using Python and - for maximum accessibility - a distributed configuration store like ZooKeeper, etcd or Consul. Database engineers, DBAs, DevOps engineers, and SREs who are looking to quickly deploy HA PostgreSQL in the datacenter-or anywhere else-will hopefully find it useful.
Patroni is a tool in the Database Tools category of a tech stack.
Patroni is an open source tool with 3.6K GitHub stars and 430 GitHub forks. Here’s a link to Patroni's open source repository on GitHub

Top Alternatives to Patroni

  • Citus

    Citus

    It's an extension to Postgres that distributes data and queries in a cluster of multiple machines. Its query engine parallelizes incoming SQL queries across these servers to enable human real-time (less than a second) responses on large datasets. ...

  • PostgreSQL

    PostgreSQL

    PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions. ...

  • Slick

    Slick

    It is a modern database query and access library for Scala. It allows you to work with stored data almost as if you were using Scala collections while at the same time giving you full control over when a database access happens and which data is transferred. ...

  • Spring Data

    Spring Data

    It makes it easy to use data access technologies, relational and non-relational databases, map-reduce frameworks, and cloud-based data services. This is an umbrella project which contains many subprojects that are specific to a given database. ...

  • Microsoft SQL Server Management Studio

    Microsoft SQL Server Management Studio

    It is an integrated environment for managing any SQL infrastructure, from SQL Server to Azure SQL Database. It provides tools to configure, monitor, and administer instances of SQL Server and databases. Use it to deploy, monitor, and upgrade the data-tier components used by your applications, as well as build queries and scripts. ...

  • Sequel Pro

    Sequel Pro

    Sequel Pro is a fast, easy-to-use Mac database management application for working with MySQL databases. ...

  • PostGIS

    PostGIS

    PostGIS is a spatial database extender for PostgreSQL object-relational database. It adds support for geographic objects allowing location queries to be run in SQL. ...

  • DataGrip

    DataGrip

    A cross-platform IDE that is aimed at DBAs and developers working with SQL databases. ...

Patroni alternatives & related posts

Citus logo

Citus

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Worry-free Postgres for SaaS
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related Citus posts

Dan Robinson
Shared insights
on
PostgreSQL
Citus
at

PostgreSQL was an easy early decision for the founding team. The relational data model fit the types of analyses they would be doing: filtering, grouping, joining, etc., and it was the database they knew best.

Shortly after adopting PG, they discovered Citus, which is a tool that makes it easy to distribute queries. Although it was a young project and a fork of Postgres at that point, Dan says the team was very available, highly expert, and it wouldn’t be very difficult to move back to PG if they needed to.

The stuff they forked was in query execution. You could treat the worker nodes like regular PG instances. Citus also gave them a ton of flexibility to make queries fast, and again, they felt the data model was the best fit for their application.

#DataStores #Databases

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Dan Robinson

At Heap, we searched for an existing tool that would allow us to express the full range of analyses we needed, index the event definitions that made up the analyses, and was a mature, natively distributed system.

After coming up empty on this search, we decided to compromise on the “maturity” requirement and build our own distributed system around Citus and sharded PostgreSQL. It was at this point that we also introduced Kafka as a queueing layer between the Node.js application servers and Postgres.

If we could go back in time, we probably would have started using Kafka on day one. One of the biggest benefits in adopting Kafka has been the peace of mind that it brings. In an analytics infrastructure, it’s often possible to make data ingestion idempotent.

In Heap’s case, that means that, if anything downstream from Kafka goes down, we won’t lose any data – it’s just going to take a bit longer to get to its destination. We also learned that you want the path between data hitting your servers and your initial persistence layer (in this case, Kafka) to be as short and simple as possible, since that is the surface area where a failure means you can lose customer data. We learned that it’s a very good fit for an analytics tool, since you can handle a huge number of incoming writes with relatively low latency. Kafka also gives you the ability to “replay” the data flow: it’s like a commit log for your whole infrastructure.

#MessageQueue #Databases #FrameworksFullStack

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related PostgreSQL posts

Jeyabalaji Subramanian

Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

Based on the above criteria, we selected the following tools to perform the end to end data replication:

We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

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Tim Abbott

We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

I can't recommend it highly enough.

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Slick logo

Slick

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Database query and access library for Scala
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PROS OF SLICK
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    CONS OF SLICK
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      Spring Data logo

      Spring Data

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      Provides a consistent approach to data access – relational, non-relational, map-reduce, and beyond
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      PROS OF SPRING DATA
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          Остап Комплікевич

          I need some advice to choose an engine for generation web pages from the Spring Boot app. Which technology is the best solution today? 1) JSP + JSTL 2) Apache FreeMarker 3) Thymeleaf Or you can suggest even other perspective tools. I am using Spring Boot, Spring Web, Spring Data, Spring Security, PostgreSQL, Apache Tomcat in my project. I have already tried to generate pages using jsp, jstl, and it went well. However, I had huge problems via carrying already created static pages, to jsp format, because of syntax. Thanks.

          See more
          Microsoft SQL Server Management Studio logo

          Microsoft SQL Server Management Studio

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          An integrated environment for managing any SQL infrastructure
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          PROS OF MICROSOFT SQL SERVER MANAGEMENT STUDIO
            No pros available
            CONS OF MICROSOFT SQL SERVER MANAGEMENT STUDIO
              No cons available

              related Microsoft SQL Server Management Studio posts

              Sequel Pro logo

              Sequel Pro

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              MySQL database management for Mac OS X
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                PostGIS logo

                PostGIS

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                Open source spatial database
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                CONS OF POSTGIS
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                  related PostGIS posts