Alternatives to Apache Solr logo

Alternatives to Apache Solr

Splunk, Lucene, Elasticsearch, MongoDB, and Apache Spark are the most popular alternatives and competitors to Apache Solr.
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What is Apache Solr and what are its top alternatives?

It uses the tools you use to make application building a snap. It is built on the battle-tested Apache Zookeeper, it makes it easy to scale up and down.
Apache Solr is a tool in the Search Engines category of a tech stack.

Top Alternatives to Apache Solr

  • Splunk

    Splunk

    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data. ...

  • Lucene

    Lucene

    Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities. ...

  • Elasticsearch

    Elasticsearch

    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack). ...

  • 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. ...

  • Apache Spark

    Apache Spark

    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning. ...

  • Azure Search

    Azure Search

    Azure Search makes it easy to add powerful and sophisticated search capabilities to your website or application. Quickly and easily tune search results and construct rich, fine-tuned ranking models to tie search results to business goals. Reliable throughput and storage provide fast search indexing and querying to support time-sensitive search scenarios. ...

  • 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. ...

  • 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. ...

Apache Solr alternatives & related posts

Splunk logo

Splunk

459
730
11
Search, monitor, analyze and visualize machine data
459
730
+ 1
11
PROS OF SPLUNK
  • 2
    API for searching logs, running reports
  • 1
    Query engine supports joining, aggregation, stats, etc
  • 1
    Query any log as key-value pairs
  • 1
    Splunk language supports string, date manip, math, etc
  • 1
    Granular scheduling and time window support
  • 1
    Alert system based on custom query results
  • 1
    Custom log parsing as well as automatic parsing
  • 1
    Dashboarding on any log contents
  • 1
    Ability to style search results into reports
  • 1
    Rich GUI for searching live logs
CONS OF SPLUNK
  • 1
    Splunk query language rich so lots to learn

related Splunk posts

Shared insights
on
KibanaKibanaSplunkSplunkGrafanaGrafana

I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

See more
Lucene logo

Lucene

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194
0
A high-performance, full-featured text search engine library written entirely in Java
151
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+ 1
0
PROS OF LUCENE
    Be the first to leave a pro
    CONS OF LUCENE
      Be the first to leave a con

      related Lucene posts

      Shared insights
      on
      SolrSolrLuceneLucene
      at

      "Slack provides two strategies for searching: Recent and Relevant. Recent search finds the messages that match all terms and presents them in reverse chronological order. If a user is trying to recall something that just happened, Recent is a useful presentation of the results.

      Relevant search relaxes the age constraint and takes into account the Lucene score of the document — how well it matches the query terms (Solr powers search at Slack). Used about 17% of the time, Relevant search performed slightly worse than Recent according to the search quality metrics we measured: the number of clicks per search and the click-through rate of the search results in the top several positions. We recognized that Relevant search could benefit from using the user’s interaction history with channels and other users — their ‘work graph’."

      See more
      Elasticsearch logo

      Elasticsearch

      26.1K
      19.8K
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      Open Source, Distributed, RESTful Search Engine
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      PROS OF ELASTICSEARCH
      • 321
        Powerful api
      • 311
        Great search engine
      • 231
        Open source
      • 213
        Restful
      • 200
        Near real-time search
      • 96
        Free
      • 83
        Search everything
      • 54
        Easy to get started
      • 45
        Analytics
      • 26
        Distributed
      • 6
        Fast search
      • 5
        More than a search engine
      • 3
        Great docs
      • 3
        Awesome, great tool
      • 3
        Easy to scale
      • 2
        Document Store
      • 2
        Nosql DB
      • 2
        Great piece of software
      • 2
        Great customer support
      • 2
        Intuitive API
      • 2
        Fast
      • 2
        Easy setup
      • 2
        Highly Available
      • 1
        Not stable
      • 1
        Scalability
      • 1
        Open
      • 1
        Reliable
      • 1
        Github
      • 1
        Elaticsearch
      • 1
        Actively developing
      • 1
        Responsive maintainers on GitHub
      • 1
        Ecosystem
      • 1
        Easy to get hot data
      • 1
        Potato
      • 0
        Community
      CONS OF ELASTICSEARCH
      • 6
        Resource hungry
      • 6
        Diffecult to get started
      • 5
        Expensive
      • 3
        Hard to keep stable at large scale

      related Elasticsearch 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
      Tymoteusz Paul
      Devops guy at X20X Development LTD · | 23 upvotes · 4.6M views

      Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

      It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

      I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

      We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

      If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

      The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

      Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

      See more
      MongoDB logo

      MongoDB

      64.8K
      54K
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      The database for giant ideas
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      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
      Apache Spark logo

      Apache Spark

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      Fast and general engine for large-scale data processing
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      PROS OF APACHE SPARK
      • 58
        Open-source
      • 48
        Fast and Flexible
      • 7
        One platform for every big data problem
      • 6
        Easy to install and to use
      • 6
        Great for distributed SQL like applications
      • 3
        Works well for most Datascience usecases
      • 2
        Machine learning libratimery, Streaming in real
      • 2
        In memory Computation
      • 0
        Interactive Query
      CONS OF APACHE SPARK
      • 3
        Speed

      related Apache Spark posts

      Eric Colson
      Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.1M views

      The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

      Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

      At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

      For more info:

      #DataScience #DataStack #Data

      See more
      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 7 upvotes · 1M views

      Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

      Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

      https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

      (Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

      See more
      Azure Search logo

      Azure Search

      63
      187
      16
      Search-as-a-service for web and mobile app development
      63
      187
      + 1
      16
      PROS OF AZURE SEARCH
      • 4
        Easy to set up
      • 3
        Auto-Scaling
      • 3
        Managed
      • 2
        Easy Setup
      • 2
        More languages
      • 2
        Lucene based search criteria
      CONS OF AZURE SEARCH
        Be the first to leave a con

        related Azure Search posts

        Redis logo

        Redis

        43.2K
        32.3K
        3.9K
        An in-memory database that persists on disk
        43.2K
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        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
        Cassandra logo

        Cassandra

        3.2K
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        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 · 140.1K 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