TrustRadius Insights for Apache Cassandra are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Pros
Greatest community and adoption: The Java-based NoSQL database has garnered a strong following with its greatest community and adoption. Many users have found it to be a highly popular choice among developers, benefiting from the extensive support and resources available.
Excellent integration with Apache Hadoop, Apache Spark, and Solr: Reviewers have consistently praised the database for its excellent integration capabilities with Apache Hadoop, Apache Spark, and Solr. This seamless integration provides a robust ecosystem of tools that enable efficient unit tests and stress testing.
Best-in-class performance across various workloads: Users have consistently highlighted the exceptional performance of this database across various read/write/mixed workloads. Its ability to provide low latency and high throughput has been widely appreciated by customers who require fast data retrieval and processing.
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Cassandra Reviews
7 Reviews
Engineering
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Cassandra is currently used for our enterprise eCommerce platform. So far our experience is good with Cassandra its an extremely powerful NoSQL Database with high performance—distributed, scalable, and highly available database platform.
Pros
Continuous data availability is extremely powerful feature of Cassandra.
Overall cost effective and low maintenance database platform.
High performance and low tolerance no SQL database.
Cons
Moving data from and to Cassandra to any relational database platform can be improved.
Database event logging can be handled more efficiently.
Likelihood to Recommend
It's perfect for big data or high volume data to load log files, event files, and streaming or video/image data. It gives really high performance dealing with big data fetches. But when you need to make table joins or you need more of a relational data structure, I do not think Cassandra will fit for that.
We use Cassandra as the NoSQL database for our use cases. We stream a lot of API data into this database and rely on the availability it gives us. It has proven to be consistent, which we use to our advantage. Cassandra can distribute data across multiple machines in an app-transparent manner, thus helping us to expand it on demand.
Pros
Cassandra is a masterless design, hence massively scalable. It is great for applications and use cases that cannot afford to lose data. There is no single point of failure.
You can add more nodes to Cassandra to linearly increase your transactions/requests. Also, it has great support across cloud regions and data centers.
Cassandra provides features like tunable consistency, data compression and CQL(Cassandra Query Language) which we use.
Cons
The underlying medium of Cassandra is a key-value store. So when you model your data, it is based on how you would want to query it and not how the data is structured. This results in a repetition of data when storing. Hence, there is no referential integrity - there is no concept of JOIN connections in Cassandra.
Data aggregation functions like SUM, MIN, MAX, AVG, and others are very costly even if possible. Hence Ad-hoc query or analysis is difficult.
Likelihood to Recommend
You should be very clear where you want to use Cassandra because there is no referential integrity (JOIN) in Cassandra. You have to model data based on how you want to query it, hence what use cases it can be used for should be considered carefully.
You can use it where you want to store log or user-behavior types of data. You can use it in heavy-write or time-series data storage. It is good in retail applications for fast product catalog inputs and lookups
Cassandra is an open-source NoSQL database solution offered by Apache. What's nice about Cassandra is its ability to host the data in multiple nodes in a ring, and changes made to a node in the ring will shard the update to the rest. For geographically dispersed architecture requiring local database storage, this can be a valuable asset which makes this NoSQL option stand above the rest.
Pros
Cassandra can preform read/writes very quick
Nodes in a ring will keep up to date by sharding information to each other
Cassandra is well suited for scalable application needing keyspace storage
Cons
Cassandra's query language is clunky, which is likely due to the nature of NoSQL.
Lacking the ability to relate data between sets makes querying harder, but this again is the nature of NoSQL.
Likelihood to Recommend
Cassandra is suited for applications that need quick read and write abilities. The key to column family relationship allows for super quick lookup and inserts. The nature of the ring cluster allows for fault tolerance, as well as geo-redundant storage. Cassandra is not well suited when needing to use the data to make relational inferences.
Used for specific product (which is used by whole organization). Addressing for column store we need for uniqueness of proprietary information that Redis and Mongo does not support.
Pros
Masterless
Schema-less
Multiple datacenter usage w/ little or no data loss
Cons
Rebuild/repair of objects (tables) in the keyspaces, allow to ignore keyspaces to repair.
Monitoring tool form opscenter support for Cassandra 3.x (or some other open source tool)
UI browser type to view data (rather than csql)
Likelihood to Recommend
[Cassandra is well suited to] schema-less dataset for large key value stores.
We wanted to use Cassandra to load millions of metrics we collect daily from our user base. After we collected the data we also needed to perform calculations and run "sql" like queries. The only database that came to mind, and does all those things well, is Cassandra.
Pros
Automatic data sharding between nodes
High availability
Python Support drivers
Cons
Managing cassandra nodes (adding, removing)
Need a separate tool to have a console (datastax opscenter)
Likelihood to Recommend
Cassandra performed very well when we were writing a ~300 GB of data per day on a 3 node cluster. If we had decided to read instead we found minor performance issues. When reading the data we expected as much. But for applications that are very read heavy we would chose a different product such as Couchbase.
I had used Cassandra in my academic projects which were related to cloud computing. I used it for a few projects on Salesforce where multi tenancy features are implemented. In such scenarios Cassandra was one the best choices for NoSql. Although we have used RDMS, the performance while using Cassandra was better.
I have simulated a few real time running apps like Facebook and Uber where I have used RDMS and Cassandra, and checked the performance using Jmeter. It clearly shows that Cassandra boosts the performance over RDMS. One thing I find difficult in Cassandra is following the documents, which are not so understandable.
Pros
Undoubtedly performance is an important reason
We have not encountered a single point of failure
Scalability of Cassandra is good which is the most important for the companies where demand is scaling day by day.
Cons
Cassandra has a wide range of asynchronous jobs and background tasks that are not scheduled by the client, the execution can be eccentric.
Because Cassandra is a key-value store, doing things like SUM, MIN, MAX, AVG and other aggregations are incredibly resource intensive if even possible to accomplish.
I think querying options for retrieving data is very limited.
Likelihood to Recommend
Well Suited Tunable Consistency Write Speed
Less Appropriate Ad-Hoc Queries Unpredictable Performance
Cassandra is used as a component of our HyperStore S3-compatible object storage system. Cassandra is installed on each node and provides the distributed system logic to determine how to store objects. The other components are primarily Java servers that we wrote that work in conjunction with Cassandra to provide a scalable, peer-to-peer, highly fault tolerant system.
Pros
Performant. In particular, write performance is very good. Recently, a lot of work to address the changing systems environment has been done to take advantage of areas like SSDs and very dense storage systems.
Distributed system logic. Multiple data centers and other common network configurations like heterogeneous nodes are handled and exploited well.
Community. Strong community with users and project contributors worldwide. The open-source and commercial software people work well together with sharing of lessons learned and improvements based on feedback.
Cons
Operational tools. Would like to see continued work to improve the operational capability for large clusters and large amounts of data. For example, analyzing the on-disk files.
Repair. Being able to run repair continuously and with greater control to avoid any spikes in resource use.
Likelihood to Recommend
Well suited for multiple data centers, large networks, heterogeneous hardware.
