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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Elasticsearch

    Score8.5 out of 10
    N/AElasticsearch is an enterprise search tool from Elastic in Mountain View, California.

    $16

    per month

    MongoDB

    Score8.8 out of 10
    N/AMongoDB is an open source document-oriented database system. It is part of the NoSQL family of database systems. Instead of storing data in tables as is done in a "classical" relational database, MongoDB stores structured data as JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster.

    $0.10

    million reads

    Pricing
    ElasticsearchMongoDB
    Editions & Modules
    Standard
    $16.00
    per month
    Gold
    $19.00
    per month
    Platinum
    $22.00
    per month
    Enterprise
    Contact Sales
    Shared
    $0
    per month
    Serverless
    $0.10million reads
    million reads
    Dedicated
    $57
    per month
    Offerings
    Pricing Offerings
    ElasticsearchMongoDB
    Free Trial
    NoYes
    Free/Freemium Version
    NoYes
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details—Fully managed, global cloud database on AWS, Azure, and GCP
    More Pricing Information
    Community Pulse
    ElasticsearchMongoDB
    Considered Both Products
    Elastic
    Chose Elasticsearch
    Elasticsearch is the most well-known and supported free data platform that we identified. We are taking advantage of community knowledge and practices.
    In terms of flexibility and breadth of use cases no other competitor came close to Elasticsearch.
    We've tried Solr in the past …
    Incentivized
    Chose Elasticsearch
    When we first evaluated Elasticsearch, we compared it with alternatives like traditional RDBMS products (Postgres, MySQL) as well as other noSQL solutions like Cassandra & MongoDB. For our use case, Elasticsearch delivered on two fronts. First, we got a world-class search …
    Incentivized
    Chose Elasticsearch
    Other services, such as Alienvault or MongoDB, are not designed to integrate as well with parsing log data. Graphite was much more difficult to work into an usable product as it does not integrate as easily with log parsing plugins. Elasticsearch had the right features to …
    Incentivized
    Chose Elasticsearch
    Even when sphinx base code is on c++ and they obtain a great performance from it, even when they have a set of plugins that allow to integrate with common database systems like MySQL, Elasticsearch is on top of license and all their experience on search. It also provides a long …
    Incentivized
    Chose Elasticsearch
    Elasticsearch and Solr are both based on Lucene, but the user community for Elasticsearch is much stronger, and setting up a cluster is easier. Splunk is very well suited for Log indexing and searching but is not nearly as flexible as Elasticsearch. Couchbase is a great NoSQL …
    Incentivized
    Chose Elasticsearch
    Search and analytics capabilities of Elasticsearch are superior to its competitors. Being open source, it is a cheaper and faster solution than other competitors. Installation is straightforward and it can be potentially deployed anywhere and everywhere! There is no need for …
    Incentivized
    Chose Elasticsearch
    Elasticsearch is much easier to set up and maintain. It provides better distributed architecture and fault tolerance, and is much faster searching.
    Incentivized
    Chose Elasticsearch
    All database systems have things they are good at, and things they aren't as good at. Riak/SOLR is great as a K/V store, but SOLR cannot handle requests as fast as ElasticSearch. In fact, SOLR is the reason we had to migrate to ElasticSearch.
    Redis is great at SET operations …
    Incentivized
    Chose Elasticsearch
    The only other competitor we researched was mongo as some of our table information is stored in an XML file, but as we were doing searching we gravitated towards Elasticsearch. We knew mongo had some of the qualifications for what we wanted, but went with Elasticsearch for …
    Incentivized
    Chose Elasticsearch
    We found Elasticsearch to be the fastest in querying text based data, allowing us to significantly speed up our APIs.
    Incentivized
    Chose Elasticsearch
    Elasticsearch is DevOps friendly; it is easy for installation and management of a node/cluster. It is very friendly for developers by providing the REST API out of the box, reducing the development time.
    Incentivized
    Chose Elasticsearch
    For our application, ElasticSearch fulfilled all the criteria we were looking for. Something that's easy to scale and flexible. I think ElasticSearch works better that Solr with modern real-time search applications. Also, ElasticSearch is easy to integrate with. ElasticSearch …
    Incentivized
    Chose Elasticsearch
    ElasticSearch was easier to scale and faster than Mongo.
    Incentivized
    MongoDB
    Chose MongoDB
    MongoDB is more of a general purpose nosql database, while Elasticsearch is a search engine oriented nosql solution.
    Percona Server for MongoDB is a more suitable solution for the enterprise field.
    Incentivized
    Chose MongoDB
    Your default choice should not be MongoDB in my opinion. Most user-facing systems are relational by nature so a well known and reliable SQL database would be easier to maintain and simpler to develop long term. If you highly value speed of development go with Firebase. If you …
    Incentivized
    Chose MongoDB
    MongoDB and Cassandra are both database system from the NoSQL family. MongoDB can be used in lots of use cases while Cassandra has a specific usage. There are some features that MongoDB provides efficiently while Cassandra doesn't and vice-versa. Like, you can update the data …
    Incentivized
    Chose MongoDB
    The way MongoDB handles the data is unique and the indexing of data is powerful.
    Incentivized
    Chose MongoDB
    Cassandra: may be better for bigger use cases, in PB range, due to our use cases being slightly smaller, we did not need this, but we highly rely on efficient indexing, and low latency, which seemed to be better based on our testing in Mongodb.
    Couchbase Server: Document …
    Incentivized
    Key User Insights
    Would buy again
    82%
    Would buy again
    14 Answers
    100%
    Would buy again
    12 Answers
    Delivers good value for the price
    100%
    Delivers good value for the price
    16 Answers
    100%
    Delivers good value for the price
    12 Answers
    Happy with the feature set
    100%
    Happy with the feature set
    17 Answers
    100%
    Happy with the feature set
    12 Answers
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    13 Answers
    100%
    Lived up to sales and marketing promises
    9 Answers
    Implementation went as expected
    87%
    Implementation went as expected
    13 Answers
    100%
    Implementation went as expected
    12 Answers
    Features
    ElasticsearchMongoDB
    NoSQL Databases
    Comparison of NoSQL Databases features of Elasticsearch and MongoDB
    Feature
    Elasticsearch
    -
    Ratings
    MongoDB
    10.0
    39 Ratings
    16% above category average
    Performance00 Ratings10.039 Ratings
    Availability00 Ratings10.039 Ratings
    Concurrency00 Ratings10.039 Ratings
    Security00 Ratings10.039 Ratings
    Scalability00 Ratings10.039 Ratings
    Data model flexibility00 Ratings10.039 Ratings
    Deployment model flexibility00 Ratings10.038 Ratings
    Best Alternatives
    ElasticsearchMongoDB
    Small Businesses
    Apache Solr
    Score7.9 out of 10
    IBM Cloudant
    Score7.4 out of 10
    Medium-sized Companies
    IBM Watson Discovery
    Score9 out of 10
    IBM Cloudant
    Score7.4 out of 10
    Enterprises
    Amazon CloudSearch
    Score8.5 out of 10
    IBM Cloudant
    Score7.4 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    ElasticsearchMongoDB
    Likelihood to Recommend
    9.0
    (48 ratings)
    10.0
    (79 ratings)
    Likelihood to Renew
    10.0
    (1 ratings)
    10.0
    (67 ratings)
    Usability
    10.0
    (1 ratings)
    10.0
    (15 ratings)
    Availability
    -
    (0 ratings)
    9.0
    (1 ratings)
    Support Rating
    7.8
    (9 ratings)
    9.6
    (13 ratings)
    Implementation Rating
    9.0
    (1 ratings)
    8.4
    (2 ratings)
    User Testimonials
    ElasticsearchMongoDB
    Likelihood to Recommend
    Elastic
    Elasticsearch is a really scalable solution that can fit a lot of needs, but the bigger and/or those needs become, the more understanding & infrastructure you will need for your instance to be running correctly. Elasticsearch is not problem-free - you can get yourself in a lot of trouble if you are not following good practices and/or if are not managing the cluster correctly. Licensing is a big decision point here as Elasticsearch is a middleware component - be sure to read the licensing agreement of the version you want to try before you commit to it. Same goes for long-term support - be sure to keep yourself in the know for this aspect you may end up stuck with an unpatched version for years.
    Incentivized
    Read full review
    MongoDB
    If asked by a colleague I would highly recommend MongoDB. MongoDB provides incredible flexibility and is quick and easy to set up. It also provides extensive documentation which is very useful for someone new to the tool. Though I've used it for years and still referenced the docs often. From my experience and the use cases I've worked on, I'd suggest using it anywhere that needs a fast, efficient storage space for non-relational data. If a relational database is needed then another tool would be more apt.
    Incentivized
    Read full review
    Pros
    Elastic
    • As I mentioned before, Elasticsearch's flexible data model is unparalleled. You can nest fields as deeply as you want, have as many fields as you want, but whatever you want in those fields (as long as it stays the same type), and all of it will be searchable and you don't need to even declare a schema beforehand!
    • Elastic, the company behind Elasticsearch, is super strong financially and they have a great team of devs and product managers working on Elasticsearch. When I first started using ES 3 years ago, I was 90% impressed and knew it would be a good fit. 3 years later, I am 200% impressed and blown away by how far it has come and gotten even better. If there are features that are missing or you don't think it's fast enough right now, I bet it'll be suitable next year because the team behind it is so dang fast!
    • Elasticsearch is really, really stable. It takes a lot to bring down a cluster. It's self-balancing algorithms, leader-election system, self-healing properties are state of the art. We've never seen network failures or hard-drive corruption or CPU bugs bring down an ES cluster.
    Incentivized
    Read full review
    MongoDB
    • Being a JSON language optimizes the response time of a query, you can directly build a query logic from the same service
    • You can install a local, database-based environment rather than the non-relational real-time bases such a firebase does not allow, the local environment is paramount since you can work without relying on the internet.
    • Forming collections in Mango is relatively simple, you do not need to know of query to work with it, since it has a simple graphic environment that allows you to manage databases for those who are not experts in console management.
    Incentivized
    Read full review
    Cons
    Elastic
    • Joining data requires duplicate de-normalized documents that make parent child relationships. It is hard and requires a lot of synchronizations
    • Tracking errors in the data in the logs can be hard, and sometimes recurring errors blow up the error logs
    • Schema changes require complete reindexing of an index
    Incentivized
    Read full review
    MongoDB
    • An aggregate pipeline can be a bit overwhelming as a newcomer.
    • There's still no real concept of joins with references/foreign keys, although the aggregate framework has a feature that is close.
    • Database management/dev ops can still be time-consuming if rolling your own deployments. (Thankfully there are plenty of providers like Compose or even MongoDB's own Atlas that helps take care of the nitty-gritty.
    Incentivized
    Read full review
    Likelihood to Renew
    Elastic
    We're pretty heavily invested in ElasticSearch at this point, and there aren't any obvious negatives that would make us reconsider this decision.
    Incentivized
    Read full review
    MongoDB
    I am looking forward to increasing our SaaS subscriptions such that I get to experience global replica sets, working in reads from secondaries, and what not. Can't wait to be able to exploit some of the power that the "Big Boys" use MongoDB for.
    Incentivized
    Read full review
    Usability
    Elastic
    To get started with Elasticsearch, you don't have to get very involved in configuring what really is an incredibly complex system under the hood. You simply install the package, run the service, and you're immediately able to begin using it. You don't need to learn any sort of query language to add data to Elasticsearch or perform some basic searching. If you're used to any sort of RESTful API, getting started with Elasticsearch is a breeze. If you've never interacted with a RESTful API directly, the journey may be a little more bumpy. Overall, though, it's incredibly simple to use for what it's doing under the covers.
    Incentivized
    Read full review
    MongoDB
    NoSQL database systems such as MongoDB lack graphical interfaces by default and therefore to improve usability it is necessary to install third-party applications to see more visually the schemas and stored documents. In addition, these tools also allow us to visualize the commands to be executed for each operation.
    Incentivized
    Read full review
    Support Rating
    Elastic
    We've only used it as an opensource tooling. We did not purchase any additional support to roll out the elasticsearch software. When rolling out the application on our platform we've used the documentation which was available online. During our test phases we did not experience any bugs or issues so we did not rely on support at all.
    Incentivized
    Read full review
    MongoDB
    Finding support from local companies can be difficult. There were times when the local company could not find a solution and we reached a solution by getting support globally. If a good local company is found, it will overcome all your problems with its global support.
    Incentivized
    Read full review
    Implementation Rating
    Elastic
    Do not mix data and master roles. Dedicate at least 3 nodes just for Master
    Incentivized
    Read full review
    MongoDB
    While the setup and configuration of MongoDB is pretty straight forward, having a vendor that performs automatic backups and scales the cluster automatically is very convenient. If you do not have a system administrator or DBA familiar with MongoDB on hand, it's a very good idea to use a 3rd party vendor that specializes in MongoDB hosting. The value is very well worth it over hosting it yourself since the cost is often reasonable among providers.
    Incentivized
    Read full review
    Alternatives Considered
    Elastic
    As far as we are concerned, Elasticsearch is the gold standard and we have barely evaluated any alternatives. You could consider it an alternative to a relational or NoSQL database, so in cases where those suffice, you don't need Elasticsearch. But if you want powerful text-based search capabilities across large data sets, Elasticsearch is the way to go.
    Incentivized
    Read full review
    MongoDB
    We have [measured] the speed in reading/write operations in high load and finally select the winner = MongoDBWe have [not] too much data but in case there will be 10 [times] more we need Cassandra. Cassandra's storage engine provides constant-time writes no matter how big your data set grows. For analytics, MongoDB provides a custom map/reduce implementation; Cassandra provides native Hadoop support.
    Read full review
    Return on Investment
    Elastic
    • We have had great luck with implementing Elasticsearch for our search and analytics use cases.
    • While the operational burden is not minimal, operating a cluster of servers, using a custom query language, writing Elasticsearch-specific bulk insert code, the performance and the relative operational ease of Elasticsearch are unparalleled.
    • We've easily saved hundreds of thousands of dollars implementing Elasticsearch vs. RDBMS vs. other no-SQL solutions for our specific set of problems.
    Incentivized
    Read full review
    MongoDB
    • Open Source w/ reasonable support costs have a direct, positive impact on the ROI (we moved away from large, monolithic, locked in licensing models)
    • You do have to balance the necessary level of HA & DR with the number of servers required to scale up and scale out. Servers cost money - so DR & HR doesn't come for free (even though it's built into the architecture of MongoDB
    Read full review
    ScreenShots

    MongoDB Screenshots

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