Alternatives to LevelDB logo

Alternatives to LevelDB

MongoDB, Redis, Symas LMDB, Badger, and RocksDB are the most popular alternatives and competitors to LevelDB.
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What is LevelDB and what are its top alternatives?

It is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values. It has been ported to a variety of Unix-based systems, macOS, Windows, and Android.
LevelDB is a tool in the Databases category of a tech stack.
LevelDB is an open source tool with 27.1K GitHub stars and 6.1K GitHub forks. Here’s a link to LevelDB's open source repository on GitHub

Top Alternatives to LevelDB

  • 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. ...

  • Redis

    Redis

    Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets. ...

  • Symas LMDB

    Symas LMDB

    It is an extraordinarily fast, memory-efficient database which is developed for the OpenLDAP Project. With memory-mapped files, it has the read performance of a pure in-memory database while retaining the persistence of standard disk-based databases. ...

  • Badger

    Badger

    Domain management you'll enjoy. Domains effectively drive the entire internet, shouldn't they be easier to manage? We thought so, and thus, Badger was born! You shouldn't have to auction off your house and sacrifice your first born to transfer domains, you should be able to press a button that says "Transfer Domain" and be done with it. That is our philosophy, and we think you will appreciate it. Stop letting domain registrars badger you, and start using... Badger! ...

  • RocksDB

    RocksDB

    RocksDB is an embeddable persistent key-value store for fast storage. RocksDB can also be the foundation for a client-server database but our current focus is on embedded workloads. RocksDB builds on LevelDB to be scalable to run on servers with many CPU cores, to efficiently use fast storage, to support IO-bound, in-memory and write-once workloads, and to be flexible to allow for innovation. ...

  • 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. ...

  • CouchDB

    CouchDB

    Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript. ...

  • Cassandra

    Cassandra

    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL. ...

LevelDB alternatives & related posts

MongoDB logo

MongoDB

64.1K
53.6K
4.1K
The database for giant ideas
64.1K
53.6K
+ 1
4.1K
PROS OF MONGODB
  • 824
    Document-oriented storage
  • 591
    No sql
  • 546
    Ease of use
  • 465
    Fast
  • 406
    High performance
  • 256
    Free
  • 215
    Open source
  • 179
    Flexible
  • 142
    Replication & high availability
  • 109
    Easy to maintain
  • 41
    Querying
  • 37
    Easy scalability
  • 36
    Auto-sharding
  • 35
    High availability
  • 31
    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
    Easy integration with Node.Js
  • 7
    Enterprise
  • 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
    Easy to Scale
  • 2
    Fast
  • 2
    Awesome
  • 2
    Managed service
  • 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
Redis logo

Redis

42.5K
32.1K
3.9K
An in-memory database that persists on disk
42.5K
32.1K
+ 1
3.9K
PROS OF REDIS
  • 875
    Performance
  • 535
    Super fast
  • 511
    Ease of use
  • 441
    In-memory cache
  • 321
    Advanced key-value cache
  • 190
    Open source
  • 179
    Easy to deploy
  • 163
    Stable
  • 153
    Free
  • 120
    Fast
  • 40
    High-Performance
  • 39
    High Availability
  • 34
    Data Structures
  • 32
    Very Scalable
  • 23
    Replication
  • 20
    Great community
  • 19
    Pub/Sub
  • 17
    "NoSQL" key-value data store
  • 14
    Hashes
  • 12
    Sets
  • 10
    Sorted Sets
  • 9
    Lists
  • 8
    BSD licensed
  • 8
    NoSQL
  • 7
    Async replication
  • 7
    Integrates super easy with Sidekiq for Rails background
  • 7
    Bitmaps
  • 6
    Open Source
  • 6
    Keys with a limited time-to-live
  • 5
    Strings
  • 5
    Lua scripting
  • 4
    Awesomeness for Free!
  • 4
    Hyperloglogs
  • 3
    outstanding performance
  • 3
    Runs server side LUA
  • 3
    Networked
  • 3
    LRU eviction of keys
  • 3
    Written in ANSI C
  • 3
    Feature Rich
  • 3
    Transactions
  • 2
    Data structure server
  • 2
    Performance & ease of use
  • 1
    Existing Laravel Integration
  • 1
    Automatic failover
  • 1
    Easy to use
  • 1
    Object [key/value] size each 500 MB
  • 1
    Simple
  • 1
    Channels concept
  • 1
    Scalable
  • 1
    Temporarily kept on disk
  • 1
    Dont save data if no subscribers are found
  • 0
    Jk
CONS OF REDIS
  • 14
    Cannot query objects directly
  • 2
    No secondary indexes for non-numeric data types
  • 1
    No WAL

related Redis posts

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

I'm working as one of the engineering leads in RunaHR. As our platform is a Saas, we thought It'd be good to have an API (We chose Ruby and Rails for this) and a SPA (built with React and Redux ) connected. We started the SPA with Create React App since It's pretty easy to start.

We use Jest as the testing framework and react-testing-library to test React components. In Rails we make tests using RSpec.

Our main database is PostgreSQL, but we also use MongoDB to store some type of data. We started to use Redis  for cache and other time sensitive operations.

We have a couple of extra projects: One is an Employee app built with React Native and the other is an internal back office dashboard built with Next.js for the client and Python in the backend side.

Since we have different frontend apps we have found useful to have Bit to document visual components and utils in JavaScript.

See more
Symas LMDB logo

Symas LMDB

11
26
0
A memory-efficient database
11
26
+ 1
0
PROS OF SYMAS LMDB
    Be the first to leave a pro
    CONS OF SYMAS LMDB
      Be the first to leave a con

      related Symas LMDB posts

      Badger logo

      Badger

      10
      26
      0
      A new way of registering and managing your domains.
      10
      26
      + 1
      0
      PROS OF BADGER
        Be the first to leave a pro
        CONS OF BADGER
          Be the first to leave a con

          related Badger posts

          RocksDB logo

          RocksDB

          88
          201
          10
          Embeddable persistent key-value store for fast storage, developed and maintained by Facebook Database Engineering Team
          88
          201
          + 1
          10
          PROS OF ROCKSDB
          • 4
            Very fast
          • 3
            Made by Facebook
          • 2
            Consistent performance
          • 1
            Ability to add logic to the database layer where needed
          CONS OF ROCKSDB
            Be the first to leave a con

            related RocksDB posts

            Thierry Schellenbach
            Shared insights
            on
            RedisRedisCassandraCassandraRocksDBRocksDB
            at

            1.0 of Stream leveraged Cassandra for storing the feed. Cassandra is a common choice for building feeds. Instagram, for instance started, out with Redis but eventually switched to Cassandra to handle their rapid usage growth. Cassandra can handle write heavy workloads very efficiently.

            Cassandra is a great tool that allows you to scale write capacity simply by adding more nodes, though it is also very complex. This complexity made it hard to diagnose performance fluctuations. Even though we had years of experience with running Cassandra, it still felt like a bit of a black box. When building Stream 2.0 we decided to go for a different approach and build Keevo. Keevo is our in-house key-value store built upon RocksDB, gRPC and Raft.

            RocksDB is a highly performant embeddable database library developed and maintained by Facebook’s data engineering team. RocksDB started as a fork of Google’s LevelDB that introduced several performance improvements for SSD. Nowadays RocksDB is a project on its own and is under active development. It is written in C++ and it’s fast. Have a look at how this benchmark handles 7 million QPS. In terms of technology it’s much more simple than Cassandra.

            This translates into reduced maintenance overhead, improved performance and, most importantly, more consistent performance. It’s interesting to note that LinkedIn also uses RocksDB for their feed.

            #InMemoryDatabases #DataStores #Databases

            See more
            SQLite logo

            SQLite

            12K
            9.6K
            528
            A software library that implements a self-contained, serverless, zero-configuration, transactional SQL database engine
            12K
            9.6K
            + 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
            PostgreSQLPostgreSQLMySQLMySQLSQLiteSQLite

            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
            Christian Stefanescu
            Head of IT at lawpilots · | 3 upvotes · 9.7K views
            Shared insights
            on
            DjangoDjangoSQLiteSQLitePostgreSQLPostgreSQL

            While I love and use PostgreSQL , I would definitely recommend having a look at SQLite as well. It can be a solid database for lots of applications and it brings some advantages in terms of handling: you don't need a server running, which makes things like testing, deploying or backing up databases much easier. Through the ORM in Django you are one abstraction level away from your database anyway and switching later on is definitely an option, but I believe SQLite is very good for pretty much all the small applications you can think of.

            See more
            CouchDB logo

            CouchDB

            429
            498
            139
            HTTP + JSON document database with Map Reduce views and peer-based replication
            429
            498
            + 1
            139
            PROS OF COUCHDB
            • 43
              JSON
            • 30
              Open source
            • 18
              Highly available
            • 12
              Partition tolerant
            • 11
              Eventual consistency
            • 7
              Sync
            • 5
              REST API
            • 4
              Attachments mechanism to docs
            • 4
              Multi master replication
            • 3
              Changes feed
            • 1
              REST interface
            • 1
              js- and erlang-views
            CONS OF COUCHDB
              Be the first to leave a con

              related CouchDB posts

              Jonathan Pugh
              Software Engineer / Project Manager / Technical Architect · | 25 upvotes · 1.6M 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
              Gabriel Pa

              We implemented our first large scale EPR application from naologic.com using CouchDB .

              Very fast, replication works great, doesn't consume much RAM, queries are blazing fast but we found a problem: the queries were very hard to write, it took a long time to figure out the API, we had to go and write our own @nodejs library to make it work properly.

              It lost most of its support. Since then, we migrated to Couchbase and the learning curve was steep but all worth it. Memcached indexing out of the box, full text search works great.

              See more
              Cassandra logo

              Cassandra

              3.2K
              3.1K
              492
              A partitioned row store. Rows are organized into tables with a required primary key.
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              492
              PROS OF CASSANDRA
              • 114
                Distributed
              • 95
                High performance
              • 80
                High availability
              • 74
                Easy scalability
              • 52
                Replication
              • 26
                Multi datacenter deployments
              • 26
                Reliable
              • 8
                OLTP
              • 7
                Open source
              • 7
                Schema optional
              • 2
                Workload separation (via MDC)
              • 1
                Fast
              CONS OF CASSANDRA
              • 2
                Reliability of replication
              • 1
                Updates

              related Cassandra posts

              Thierry Schellenbach
              Shared insights
              on
              RedisRedisCassandraCassandraRocksDBRocksDB
              at

              1.0 of Stream leveraged Cassandra for storing the feed. Cassandra is a common choice for building feeds. Instagram, for instance started, out with Redis but eventually switched to Cassandra to handle their rapid usage growth. Cassandra can handle write heavy workloads very efficiently.

              Cassandra is a great tool that allows you to scale write capacity simply by adding more nodes, though it is also very complex. This complexity made it hard to diagnose performance fluctuations. Even though we had years of experience with running Cassandra, it still felt like a bit of a black box. When building Stream 2.0 we decided to go for a different approach and build Keevo. Keevo is our in-house key-value store built upon RocksDB, gRPC and Raft.

              RocksDB is a highly performant embeddable database library developed and maintained by Facebook’s data engineering team. RocksDB started as a fork of Google’s LevelDB that introduced several performance improvements for SSD. Nowadays RocksDB is a project on its own and is under active development. It is written in C++ and it’s fast. Have a look at how this benchmark handles 7 million QPS. In terms of technology it’s much more simple than Cassandra.

              This translates into reduced maintenance overhead, improved performance and, most importantly, more consistent performance. It’s interesting to note that LinkedIn also uses RocksDB for their feed.

              #InMemoryDatabases #DataStores #Databases

              See more
              Umair Iftikhar
              Technical Architect at Vappar · | 3 upvotes · 136.6K views

              Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.

              My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.

              See more