Alternatives to Elasticsearch logo

Alternatives to Elasticsearch

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

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).
Elasticsearch is a tool in the Search as a Service category of a tech stack.
Elasticsearch is an open source tool with 57.2K GitHub stars and 20.8K GitHub forks. Here’s a link to Elasticsearch's open source repository on GitHub

Top Alternatives to Elasticsearch

  • Datadog

    Datadog

    Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog! ...

  • Solr

    Solr

    Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites. ...

  • 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. ...

  • 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. ...

  • Algolia

    Algolia

    Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard. ...

  • Splunk

    Splunk

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

  • Kibana

    Kibana

    Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch. ...

  • 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. ...

Elasticsearch alternatives & related posts

Datadog logo

Datadog

6.1K
5.3K
792
Unify logs, metrics, and traces from across your distributed infrastructure.
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PROS OF DATADOG
  • 133
    Monitoring for many apps (databases, web servers, etc)
  • 104
    Easy setup
  • 84
    Powerful ui
  • 81
    Powerful integrations
  • 68
    Great value
  • 52
    Great visualization
  • 44
    Events + metrics = clarity
  • 40
    Custom metrics
  • 39
    Notifications
  • 37
    Flexibility
  • 17
    Free & paid plans
  • 14
    Great customer support
  • 13
    Makes my life easier
  • 8
    Adapts automatically as i scale up
  • 8
    Easy setup and plugins
  • 6
    Super easy and powerful
  • 5
    In-context collaboration
  • 5
    AWS support
  • 4
    Rich in features
  • 3
    Docker support
  • 3
    Cost
  • 3
    Best than others
  • 2
    Source control and bug tracking
  • 2
    Easy to Analyze
  • 2
    Expensive
  • 2
    Cute logo
  • 2
    Good for Startups
  • 2
    Free setup
  • 2
    Monitor almost everything
  • 2
    Automation tools
  • 2
    Simple, powerful, great for infra
  • 2
    Full visibility of applications
  • 1
    Best in the field
CONS OF DATADOG
  • 16
    Expensive
  • 3
    No errors exception tracking
  • 2
    External Network Goes Down You Wont Be Logging
  • 1
    Complicated

related Datadog posts

Robert Zuber

Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

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We are looking for a centralised monitoring solution for our application deployed on Amazon EKS. We would like to monitor using metrics from Kubernetes, AWS services (NeptuneDB, AWS Elastic Load Balancing (ELB), Amazon EBS, Amazon S3, etc) and application microservice's custom metrics.

We are expected to use around 80 microservices (not replicas). I think a total of 200-250 microservices will be there in the system with 10-12 slave nodes.

We tried Prometheus but it looks like maintenance is a big issue. We need to manage scaling, maintaining the storage, and dealing with multiple exporters and Grafana. I felt this itself needs few dedicated resources (at least 2-3 people) to manage. Not sure if I am thinking in the correct direction. Please confirm.

You mentioned Datadog and Sysdig charges per host. Does it charge per slave node?

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Solr logo

Solr

673
538
125
A blazing-fast, open source enterprise search platform
673
538
+ 1
125
PROS OF SOLR
  • 35
    Powerful
  • 22
    Indexing and searching
  • 20
    Scalable
  • 19
    Customizable
  • 13
    Enterprise Ready
  • 5
    Apache Software Foundation
  • 5
    Restful
  • 4
    Great Search engine
  • 2
    Security built-in
CONS OF SOLR
    Be the first to leave a con

    related Solr posts

    Ganesa Vijayakumar
    Full Stack Coder | Module Lead · | 19 upvotes · 2.5M views

    I'm planning to create a web application and also a mobile application to provide a very good shopping experience to the end customers. Shortly, my application will be aggregate the product details from difference sources and giving a clear picture to the user that when and where to buy that product with best in Quality and cost.

    I have planned to develop this in many milestones for adding N number of features and I have picked my first part to complete the core part (aggregate the product details from different sources).

    As per my work experience and knowledge, I have chosen the followings stacks to this mission.

    UI: I would like to develop this application using React, React Router and React Native since I'm a little bit familiar on this and also most importantly these will help on developing both web and mobile apps. In addition, I'm gonna use the stacks JavaScript, jQuery, jQuery UI, jQuery Mobile, Bootstrap wherever required.

    Service: I have planned to use Java as the main business layer language as I have 7+ years of experience on this I believe I can do better work using Java than other languages. In addition, I'm thinking to use the stacks Node.js.

    Database and ORM: I'm gonna pick MySQL as DB and Hibernate as ORM since I have a piece of good knowledge and also work experience on this combination.

    Search Engine: I need to deal with a large amount of product data and it's in-detailed info to provide enough details to end user at the same time I need to focus on the performance area too. so I have decided to use Solr as a search engine for product search and suggestions. In addition, I'm thinking to replace Solr by Elasticsearch once explored/reviewed enough about Elasticsearch.

    Host: As of now, my plan to complete the application with decent features first and deploy it in a free hosting environment like Docker and Heroku and then once it is stable then I have planned to use the AWS products Amazon S3, EC2, Amazon RDS and Amazon Route 53. I'm not sure about Microsoft Azure that what is the specialty in it than Heroku and Amazon EC2 Container Service. Anyhow, I will do explore these once again and pick the best suite one for my requirement once I reached this level.

    Build and Repositories: I have decided to choose Apache Maven and Git as these are my favorites and also so popular on respectively build and repositories.

    Additional Utilities :) - I would like to choose Codacy for code review as their Startup plan will be very helpful to this application. I'm already experienced with Google CheckStyle and SonarQube even I'm looking something on Codacy.

    Happy Coding! Suggestions are welcome! :)

    Thanks, Ganesa

    See more
    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
    Lucene logo

    Lucene

    151
    193
    0
    A high-performance, full-featured text search engine library written entirely in Java
    151
    193
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    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
        MongoDB logo

        MongoDB

        64.2K
        53.4K
        4.1K
        The database for giant ideas
        64.2K
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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.

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        Algolia logo

        Algolia

        1K
        945
        695
        Developer-friendly API and complete set of tools for building search
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        945
        + 1
        695
        PROS OF ALGOLIA
        • 125
          Ultra fast
        • 95
          Super easy to implement
        • 73
          Modern search engine
        • 71
          Excellent support
        • 70
          Easy setup, fast and relevant
        • 46
          Typos handling
        • 40
          Search analytics
        • 31
          Designed to search records, not pages
        • 30
          Multiple datacenters
        • 30
          Distributed Search Network
        • 10
          Smart Highlighting
        • 9
          Search as you type
        • 8
          Instantsearch.js
        • 8
          Multi-attributes
        • 6
          Super fast, easy to set up
        • 5
          Amazing uptime
        • 5
          Database search
        • 4
          Realtime
        • 4
          Great documentation
        • 4
          Highly customizable
        • 4
          Github-awesome-autocomple
        • 3
          Powerful Search
        • 3
          Beautiful UI
        • 3
          Places.js
        • 2
          Integrates with just about everything
        • 2
          Awesome aanltiycs and typos hnadling
        • 1
          Fast response time
        • 1
          Smooth platform
        • 1
          Github integration
        • 1
          Developer-friendly frontend libraries
        CONS OF ALGOLIA
        • 10
          Expensive

        related Algolia posts

        Julien DeFrance
        Principal Software Engineer at Tophatter · | 16 upvotes · 2.4M views

        Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

        I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

        For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

        Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

        Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

        Future improvements / technology decisions included:

        Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

        As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

        One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

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        Tim Specht
        ‎Co-Founder and CTO at Dubsmash · | 16 upvotes · 314.5K views

        Although we were using Elasticsearch in the beginning to power our in-app search, we moved this part of our processing over to Algolia a couple of months ago; this has proven to be a fantastic choice, letting us build search-related features with more confidence and speed.

        Elasticsearch is only used for searching in internal tooling nowadays; hosting and running it reliably has been a task that took up too much time for us in the past and fine-tuning the results to reach a great user-experience was also never an easy task for us. With Algolia we can flexibly change ranking methods on the fly and can instead focus our time on fine-tuning the experience within our app.

        Memcached is used in front of most of the API endpoints to cache responses in order to speed up response times and reduce server-costs on our side.

        #SearchAsAService

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        Splunk logo

        Splunk

        459
        722
        11
        Search, monitor, analyze and visualize machine data
        459
        722
        + 1
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        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.

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        Kibana logo

        Kibana

        14.8K
        11.4K
        255
        Visualize your Elasticsearch data and navigate the Elastic Stack
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        11.4K
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        PROS OF KIBANA
        • 88
          Easy to setup
        • 61
          Free
        • 44
          Can search text
        • 21
          Has pie chart
        • 13
          X-axis is not restricted to timestamp
        • 8
          Easy queries and is a good way to view logs
        • 6
          Supports Plugins
        • 3
          More "user-friendly"
        • 3
          Can build dashboards
        • 3
          Dev Tools
        • 2
          Easy to drill-down
        • 2
          Out-of-Box Dashboards/Analytics for Metrics/Heartbeat
        • 1
          Up and running
        CONS OF KIBANA
        • 5
          Unintuituve
        • 3
          Elasticsearch is huge
        • 3
          Works on top of elastic only
        • 2
          Hardweight UI

        related Kibana posts

        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.

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        Patrick Sun
        Software Engineer at Stitch Fix · | 11 upvotes · 466.5K views

        Elasticsearch's built-in visualization tool, Kibana, is robust and the appropriate tool in many cases. However, it is geared specifically towards log exploration and time-series data, and we felt that its steep learning curve would impede adoption rate among data scientists accustomed to writing SQL. The solution was to create something that would replicate some of Kibana's essential functionality while hiding Elasticsearch's complexity behind SQL-esque labels and terminology ("table" instead of "index", "group by" instead of "sub-aggregation") in the UI.

        Elasticsearch's API is really well-suited for aggregating time-series data, indexing arbitrary data without defining a schema, and creating dashboards. For the purpose of a data exploration backend, Elasticsearch fits the bill really well. Users can send an HTTP request with aggregations and sub-aggregations to an index with millions of documents and get a response within seconds, thus allowing them to rapidly iterate through their data.

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        Cassandra logo

        Cassandra

        3.2K
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        491
        A partitioned row store. Rows are organized into tables with a required primary key.
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        491
        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

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        Umair Iftikhar
        Technical Architect at Vappar · | 3 upvotes · 134.7K 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.

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