Alternatives to AlaSQL logo

Alternatives to AlaSQL

SQLite, Lovefield, Pouchdb, IndexedDB, and LokiJS are the most popular alternatives and competitors to AlaSQL.
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What is AlaSQL and what are its top alternatives?

Open source SQL database for Javascript with a strong focus on query speed and datasource flexibility for relational data, schemaless data, and graph data. Works in your browser, Node.js and Cordova. Handles both traditional relational tables and nested JSON data (NoSQL). Export, store, and import data from localStorage, IndexedDB, and Excel.
AlaSQL is a tool in the Databases category of a tech stack.
AlaSQL is an open source tool with 5.5K GitHub stars and 545 GitHub forks. Here’s a link to AlaSQL's open source repository on GitHub

Top Alternatives to AlaSQL

  • SQLite

    SQLite

    SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file. ...

  • Lovefield

    Lovefield

    Written in JavaScript, works cross-browser. Provides SQL-like APIs that are fast, safe, and easy to use. ...

  • Pouchdb

    Pouchdb

    PouchDB enables applications to store data locally while offline, then synchronize it with CouchDB and compatible servers when the application is back online, keeping the user's data in sync no matter where they next login. ...

  • IndexedDB

    IndexedDB

    This API uses indexes to enable high-performance searches of this data. While Web Storage is useful for storing smaller amounts of data, it is less useful for storing larger amounts of structured data. ...

  • LokiJS

    LokiJS

    LokiJS is a document oriented database written in javascript, published under MIT License. Its purpose is to store javascript objects as documents in a nosql fashion and retrieve them with a similar mechanism. Runs in node (including cordova/phonegap and node-webkit), nativescript and the browser. ...

  • MySQL

    MySQL

    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software. ...

  • 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. ...

  • MongoDB

    MongoDB

    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding. ...

AlaSQL alternatives & related posts

SQLite logo

SQLite

10.8K
8.4K
528
A software library that implements a self-contained, serverless, zero-configuration, transactional SQL database engine
10.8K
8.4K
+ 1
528
PROS OF SQLITE
  • 160
    Lightweight
  • 134
    Portable
  • 121
    Simple
  • 80
    Sql
  • 28
    Preinstalled on iOS and Android
  • 2
    Tcl integration
  • 1
    Free
  • 1
    Telefon
  • 1
    Portable A database on my USB 'love it'
CONS OF SQLITE
  • 2
    Not for multi-process of multithreaded apps
  • 1
    Needs different binaries for each platform

related SQLite posts

Dimelo Waterson
Shared insights
on
PostgreSQL
MySQL
SQLite

I need to add a DBMS to my stack, but I don't know which. I'm tempted to learn SQLite since it would be useful to me with its focus on local access without concurrency. However, doing so feels like I would be defeating the purpose of trying to expand my skill set since it seems like most enterprise applications have the opposite requirements.

To be able to apply what I learn to more projects, what should I try to learn? MySQL? PostgreSQL? Something else? Is there a comfortable middle ground between high applicability and ease of use?

See more
Daniel Quinn
Senior Developer at Workfinder · | 2 upvotes · 371.3K views

SQLite is a tricky beast. It's great if you're working single-threaded, but a Terrible Idea if you've got more than one concurrent connection. You use it because it's easy to setup, light, and portable (it's just a file).

In Paperless, we've built a self-hosted web application, so it makes sense to standardise on something small & light, and as we don't have to worry about multiple connections (it's just you using the app), it's a perfect fit.

For users wanting to scale Paperless up to a multi-user environment though, we do provide the hooks to switch to PostgreSQL .

See more
Lovefield logo

Lovefield

2
27
3
A relational database for web apps, by Google
2
27
+ 1
3
PROS OF LOVEFIELD
  • 2
    Relational database
  • 1
    No servers
CONS OF LOVEFIELD
  • 1
    Limited memory store

related Lovefield posts

Pouchdb logo

Pouchdb

99
182
5
Open-source JavaScript database inspired by Apache CouchDB that's designed to run well within the browser
99
182
+ 1
5
PROS OF POUCHDB
  • 1
    Offline cache
  • 1
    Very fast
  • 1
    JSON
  • 1
    Free
  • 1
    Repication
CONS OF POUCHDB
    Be the first to leave a con

    related Pouchdb posts

    Jonathan Pugh
    Software Engineer / Project Manager / Technical Architect · | 25 upvotes · 1.4M views

    I needed to choose a full stack of tools for cross platform mobile application design & development. After much research and trying different tools, these are what I came up with that work for me today:

    For the client coding I chose Framework7 because of its performance, easy learning curve, and very well designed, beautiful UI widgets. I think it's perfect for solo development or small teams. I didn't like React Native. It felt heavy to me and rigid. Framework7 allows the use of #CSS3, which I think is the best technology to come out of the #WWW movement. No other tech has been able to allow designers and developers to develop such flexible, high performance, customisable user interface elements that are highly responsive and hardware accelerated before. Now #CSS3 includes variables and flexboxes it is truly a powerful language and there is no longer a need for preprocessors such as #SCSS / #Sass / #less. React Native contains a very limited interpretation of #CSS3 which I found very frustrating after using #CSS3 for some years already and knowing its powerful features. The other very nice feature of Framework7 is that you can even build for the browser if you want your app to be available for desktop web browsers. The latest release also includes the ability to build for #Electron so you can have MacOS, Windows and Linux desktop apps. This is not possible with React Native yet.

    Framework7 runs on top of Apache Cordova. Cordova and webviews have been slated as being slow in the past. Having a game developer background I found the tweeks to make it run as smooth as silk. One of those tweeks is to use WKWebView. Another important one was using srcset on images.

    I use #Template7 for the for the templating system which is a no-nonsense mobile-centric #HandleBars style extensible templating system. It's easy to write custom helpers for, is fast and has a small footprint. I'm not forced into a new paradigm or learning some new syntax. It operates with standard JavaScript, HTML5 and CSS 3. It's written by the developer of Framework7 and so dovetails with it as expected.

    I configured TypeScript to work with the latest version of Framework7. I consider TypeScript to be one of the best creations to come out of Microsoft in some time. They must have an amazing team working on it. It's very powerful and flexible. It helps you catch a lot of bugs and also provides code completion in supporting IDEs. So for my IDE I use Visual Studio Code which is a blazingly fast and silky smooth editor that integrates seamlessly with TypeScript for the ultimate type checking setup (both products are produced by Microsoft).

    I use Webpack and Babel to compile the JavaScript. TypeScript can compile to JavaScript directly but Babel offers a few more options and polyfills so you can use the latest (and even prerelease) JavaScript features today and compile to be backwards compatible with virtually any browser. My favorite recent addition is "optional chaining" which greatly simplifies and increases readability of a number of sections of my code dealing with getting and setting data in nested objects.

    I use some Ruby scripts to process images with ImageMagick and pngquant to optimise for size and even auto insert responsive image code into the HTML5. Ruby is the ultimate cross platform scripting language. Even as your scripts become large, Ruby allows you to refactor your code easily and make it Object Oriented if necessary. I find it the quickest and easiest way to maintain certain aspects of my build process.

    For the user interface design and prototyping I use Figma. Figma has an almost identical user interface to #Sketch but has the added advantage of being cross platform (MacOS and Windows). Its real-time collaboration features are outstanding and I use them a often as I work mostly on remote projects. Clients can collaborate in real-time and see changes I make as I make them. The clickable prototyping features in Figma are also very well designed and mean I can send clickable prototypes to clients to try user interface updates as they are made and get immediate feedback. I'm currently also evaluating the latest version of #AdobeXD as an alternative to Figma as it has the very cool auto-animate feature. It doesn't have real-time collaboration yet, but I heard it is proposed for 2019.

    For the UI icons I use Font Awesome Pro. They have the largest selection and best looking icons you can find on the internet with several variations in styles so you can find most of the icons you want for standard projects.

    For the backend I was using the #GraphCool Framework. As I later found out, #GraphQL still has some way to go in order to provide the full power of a mature graph query language so later in my project I ripped out #GraphCool and replaced it with CouchDB and Pouchdb. Primarily so I could provide good offline app support. CouchDB with Pouchdb is very flexible and efficient combination and overcomes some of the restrictions I found in #GraphQL and hence #GraphCool also. The most impressive and important feature of CouchDB is its replication. You can configure it in various ways for backups, fault tolerance, caching or conditional merging of databases. CouchDB and Pouchdb even supports storing, retrieving and serving binary or image data or other mime types. This removes a level of complexity usually present in database implementations where binary or image data is usually referenced through an #HTML5 link. With CouchDB and Pouchdb apps can operate offline and sync later, very efficiently, when the network connection is good.

    I use PhoneGap when testing the app. It auto-reloads your app when its code is changed and you can also install it on Android phones to preview your app instantly. iOS is a bit more tricky cause of Apple's policies so it's not available on the App Store, but you can build it and install it yourself to your device.

    So that's my latest mobile stack. What tools do you use? Have you tried these ones?

    See more
    Mike Endale
    Shared insights
    on
    Android SDK
    Realm
    Pouchdb
    at

    We are building an offline-first Android SDK app. The solution we're working on runs on a mobile device in areas where internet connectivity is intermittent or does not exist. The applications needs to be able to collect data and when it reaches a home base or finds internet connectivity, we'll sync it with the host.

    We've heard Realm and Pouchdb could be a good solution, but we are curious if anyone has any experience with either or have another path forward.

    See more
    IndexedDB logo

    IndexedDB

    25
    54
    0
    A low-level API for client-side storage of significant amounts of structured data
    25
    54
    + 1
    0
    PROS OF INDEXEDDB
      Be the first to leave a pro
      CONS OF INDEXEDDB
        Be the first to leave a con

        related IndexedDB posts

        LokiJS logo

        LokiJS

        18
        35
        2
        In-memory JavaScript Datastore with Persistence
        18
        35
        + 1
        2
        PROS OF LOKIJS
        • 2
          Can query the objects directly
        CONS OF LOKIJS
          Be the first to leave a con

          related LokiJS posts

          MySQL logo

          MySQL

          75.4K
          59.6K
          3.7K
          The world's most popular open source database
          75.4K
          59.6K
          + 1
          3.7K
          PROS OF MYSQL
          • 790
            Sql
          • 673
            Free
          • 557
            Easy
          • 527
            Widely used
          • 485
            Open source
          • 180
            High availability
          • 158
            Cross-platform support
          • 103
            Great community
          • 78
            Secure
          • 75
            Full-text indexing and searching
          • 25
            Fast, open, available
          • 14
            SSL support
          • 13
            Reliable
          • 13
            Robust
          • 8
            Enterprise Version
          • 7
            Easy to set up on all platforms
          • 2
            NoSQL access to JSON data type
          • 1
            Replica Support
          • 1
            Easy, light, scalable
          • 1
            Relational database
          • 1
            Sequel Pro (best SQL GUI)
          CONS OF MYSQL
          • 14
            Owned by a company with their own agenda
          • 1
            Can't roll back schema changes

          related MySQL posts

          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.

          See more
          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 21 upvotes · 981.7K views

          Our most popular (& controversial!) article to date on the Uber Engineering blog in 3+ yrs. Why we moved from PostgreSQL to MySQL. In essence, it was due to a variety of limitations of Postgres at the time. Fun fact -- earlier in Uber's history we'd actually moved from MySQL to Postgres before switching back for good, & though we published the article in Summer 2016 we haven't looked back since:

          The early architecture of Uber consisted of a monolithic backend application written in Python that used Postgres for data persistence. Since that time, the architecture of Uber has changed significantly, to a model of microservices and new data platforms. Specifically, in many of the cases where we previously used Postgres, we now use Schemaless, a novel database sharding layer built on top of MySQL (https://eng.uber.com/schemaless-part-one/). In this article, we’ll explore some of the drawbacks we found with Postgres and explain the decision to build Schemaless and other backend services on top of MySQL:

          https://eng.uber.com/mysql-migration/

          See more
          PostgreSQL logo

          PostgreSQL

          56.8K
          44.8K
          3.5K
          A powerful, open source object-relational database system
          56.8K
          44.8K
          + 1
          3.5K
          PROS OF POSTGRESQL
          • 753
            Relational database
          • 506
            High availability
          • 436
            Enterprise class database
          • 379
            Sql
          • 298
            Sql + nosql
          • 171
            Great community
          • 145
            Easy to setup
          • 129
            Heroku
          • 128
            Secure by default
          • 111
            Postgis
          • 48
            Supports Key-Value
          • 46
            Great JSON support
          • 32
            Cross platform
          • 29
            Extensible
          • 26
            Replication
          • 24
            Triggers
          • 22
            Rollback
          • 21
            Multiversion concurrency control
          • 20
            Open source
          • 17
            Heroku Add-on
          • 14
            Stable, Simple and Good Performance
          • 13
            Powerful
          • 12
            Lets be serious, what other SQL DB would you go for?
          • 9
            Good documentation
          • 7
            Intelligent optimizer
          • 7
            Scalable
          • 6
            Transactional DDL
          • 6
            Modern
          • 6
            Reliable
          • 5
            Free
          • 5
            One stop solution for all things sql no matter the os
          • 4
            Relational database with MVCC
          • 3
            Faster Development
          • 3
            Full-Text Search
          • 3
            Developer friendly
          • 2
            Excellent source code
          • 2
            search
          • 2
            Great DB for Transactional system or Application
          • 1
            Full-text
          • 1
            Free version
          • 1
            Text
          • 1
            Open-source
          CONS OF POSTGRESQL
          • 9
            Table/index bloatings

          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!

          See more
          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.

          See more
          MongoDB logo

          MongoDB

          56.6K
          46.2K
          4.1K
          The database for giant ideas
          56.6K
          46.2K
          + 1
          4.1K
          PROS OF MONGODB
          • 823
            Document-oriented storage
          • 589
            No sql
          • 545
            Ease of use
          • 463
            Fast
          • 405
            High performance
          • 253
            Free
          • 214
            Open source
          • 178
            Flexible
          • 140
            Replication & high availability
          • 108
            Easy to maintain
          • 40
            Querying
          • 36
            Easy scalability
          • 35
            Auto-sharding
          • 34
            High availability
          • 30
            Map/reduce
          • 26
            Document database
          • 24
            Easy setup
          • 24
            Full index support
          • 15
            Reliable
          • 14
            Fast in-place updates
          • 13
            Agile programming, flexible, fast
          • 11
            No database migrations
          • 7
            Enterprise
          • 7
            Easy integration with Node.Js
          • 5
            Enterprise Support
          • 4
            Great NoSQL DB
          • 3
            Aggregation Framework
          • 3
            Support for many languages through different drivers
          • 3
            Drivers support is good
          • 2
            Schemaless
          • 2
            Fast
          • 2
            Awesome
          • 2
            Managed service
          • 2
            Easy to Scale
          • 1
            Consistent
          CONS OF MONGODB
          • 5
            Very slowly for connected models that require joins
          • 3
            Not acid compliant
          • 1
            Proprietary query language

          related MongoDB 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!

          See more
          Robert Zuber

          We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

          As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

          When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

          See more