Cassandra Reviews

73 Ratings
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Reviews (1-16 of 16)

Priti Asai / Thakkar | TrustRadius Reviewer
Score 8 out of 10
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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.
  • 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.
  • Moving data from and to Cassandra to any relational database platform can be improved.
  • Database event logging can be handled more efficiently.
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.
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Glen Kim | TrustRadius Reviewer
March 16, 2019

Cassandra at scale

Score 8 out of 10
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It’s one of the database platforms we offer to the development community in our organization. We have various selections when it comes to databases including DB2, SQL Server, Oracle, and hadoop for data warehousing. Cassandra becomes the choice when developers want to use a highly available NoSQL db.
  • Availability
  • Fast performance
  • Horizontal scalability
  • Memory first
  • Partition based
  • Dealing with tombstone
  • Maintenance/upgrade
  • Compaction and repair
We use it for collecting user preferences on our website which can be quickly reused. It's also well suited for document ID lookup systems. It’s not good for high consistency level of information like account balance in your banking system.
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Feng Cai | TrustRadius Reviewer
February 26, 2019

Pretty good software

Score 8 out of 10
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Cassandra is used in my organization by my department to handle data that is not in a standard RDMBS format.
  • Runs on commodity hardware
  • Build in fault tolerance
  • Can grow horizontally
  • It is a bit difficult for people that come from the SQL world.
  • Managing anti-entropy repair is still a bit of a challenge.
  • Better security patches.
Nothing beats software that works and charges nothing. It handles data that is not fit for traditional RDBMS. However, not a lot of employees know how to use it efficiently.
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Dhruba Jyoti Nag | TrustRadius Reviewer
Score 9 out of 10
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Cassandra is a NoSQL database which is used to store a large amount of data quickly. It has a very fast write speed, allowing a large volume of data storage within a small amount of time. It is tunable and can be used to store data. It is more suitable for storing flat data rather than relational data.
  • Write speed. Cassandra is very fast while writing data due to its unique architecture.
  • Tunable consistency - During data replication, consistency can be tuned for a particular data set to be available during an outage.
  • CQL - cassandra query language is a subset of SQL and eases the transition from a more traditional database.
  • Aggregation functions are not very efficient.
  • Ad-hoc queries do not perform well. Queries which were visualized while designing the databases only perform well.
  • Performance is unpredictable.
Cassandra is well suited to storing a large volume of data within a very small period of time. It is relatively fast and the data consistency can be tuned for datasets for custom availability during an outage. It can be interacted with using CQL-- Cassandra query language-- which is similar to SQL, and thus transition is easier. It however performs less during aggregation and querying.
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Anonymous | TrustRadius Reviewer
Score 8 out of 10
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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.
  • 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.
  • 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.
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
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yixiang Shan | TrustRadius Reviewer
Score 8 out of 10
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We use Cassandra to build a fully functional POC (with the continuous production level volume of feeding data) for a shipment cloud concept for Fedex's EMEA region. This solution is composed of two parts, we use an IMDG product to keep the latest transaction of all shipments' latest "status" while we use Cassandra as our long-term transaction storage to keep all historical shipment status update events. On top of those InMemory and NoSQL storage, we built one unified RESTful based service, which depends on the user's query needs, either/and/or query the IMDG for the latest status of the shipment or query the Cassandra for the history of the shipment. Also, the Cassandra is used as the "backup" of the IMDG, in case the IMDG part is fully crashed (the worst scenario). Thanks to the time series way of persisting the data in Cassandra, we still can extract the "latest" status of a shipment from Cassandra's full transaction history with reasonable performance (slower than IMDG but much quicker than the traditional relational database).
  • Cassandra is very strong for saving the time series based transaction data model, simply by reversing the time series order when creating the data table, we can very quickly fetch the "latest" records even from millions of associated transactions because the latest record is always at the top of the search. By combining with the TTL feature of the Cassandra column, it is easy to "auto" delete the old data.
  • Cassandra combines the key-value store from Amazon's DynamoDB with the column family data model from the Google's BigTable, which makes it easy to manage both structured and non-structured data model efficiently.
  • By using the DataStax Enterprise version provided Solr integration, it can even solve some ad-hoc query needs which may not be fully taken into account at the beginning of the project when the data table is created. This extremely adds more room to play for a large enterprise or project which does require some flexibility in the practical context.
  • The linear scalability provided by Cassandra, allowing us to easily scale up/down the cluster by simply adding/removing the servers.
  • The throughput for both the read/write performance of Cassandra is quite good.
  • Managing the big cluster of Cassandra , even with the DataStax Enterprise Version, is still quite challenging for a maintenance team, considering the frequent version upgrade (even in the rolling fashion) and more frequent auto-repair, for me on this area, a powerful tool should be provided to "automate" this process as much as possible.
  • The TTL design is good, however the pain is if the TTL is set on some data already inserted, it can not be simply updated. Unless that data is reinserted again, this fact causes a lot of issues in case the business strategy is changed which requires the purge strategy to be updated also.
  • As the nature of Cassandra is still Java based, the GC sometimes eats some performance, if Cassandra can allow using more non-Heap memory space, to reduce the GC efforts which will free more power on the hardware.
  • The default indexing strategy for JSON formatted data in the DataStax's Solr integration is not available. At this moment we have to implement our own to support our JSON text stored. We extract the key field from our data which might be required to be ad-hoc searched, converting them into the JSON format (only one level Map), and save them into the Cassandra column. On top of that we want Solr to index the key of each token.
For the scenarios which need ACID support, maybe Cassandra is not the best, but for an insert only (time series based) transaction case and requirements to cope with the unpredictable data model/structure changes of the future, then Cassandra is one of the best options. If you only use the open source version of Cassandra, then without Solr integrated, you need to know your search query before you create the table, if that's not possible then Cassandra or other NoSQL DB might not your right choice.
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Ravi Reddy | TrustRadius Reviewer
September 27, 2017

Cassandra Usage and Needs

Score 8 out of 10
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We are using Cassandra based on the requirements and data availability to the application (based on queries for search).
  • Cassandra lot of API's ready available for map reducing queries (like materialized queries).
  • Cassandra uses ring architecture approach, there is no master-slave approach (like HBase). If data is published on the node, the data will get synced with other nodes in the ring architecture, compared to HBase which has a dedicated master node to orchestrate the data into its slaves.
  • Write Speed
  • Multi Data Center Replication
  • Tunable Consistency
  • Integrates with JVM because it's written in Java
  • Cassandra Query Language is a subset of SQL query (less learning curve)
  • No Ad-Hoc Queries: Cassandra data storage layer is basically a key-value storage system. This means that you must "model" your data around the queries you want to surface, rather than around the structure of the data itself.
  • There are no aggregations queries available in Cassandra.
  • Not fit for transactional data.
Cassandra data storage layer is basically a key-value storage system. This means that you must model your data around the queries you want to surface, rather than around the structure of the data itself. This can lead to storing the data multiple times in different ways to be able to satisfy the requirements of your application.
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Anson Abraham | TrustRadius Reviewer
March 16, 2017

review of cassandra

Score 7 out of 10
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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.
  • Masterless
  • Schema-less
  • Multiple datacenter usage w/ little or no data loss
  • 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)
[Cassandra is well suited to] schema-less dataset for large key value stores.
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Abdel Kamel | TrustRadius Reviewer
Score 8 out of 10
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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.
  • Automatic data sharding between nodes
  • High availability
  • Python Support drivers
  • Managing cassandra nodes (adding, removing)
  • Need a separate tool to have a console (datastax opscenter)
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.
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Rekha Joshi | TrustRadius Reviewer
Score 8 out of 10
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Apache Cassandra is used extensively across the whole of our organization. It is used for various critical use cases and platform solutions where we are creating highly available, linearly scalable systems with tunable consistency. We have used it actively and rigorously for products within tax domain, small businesses, profile platforms, AB testing platforms and it is being used across product groups with great success!
  • As a Java based NoSQL database it has the greatest community and adoption. Coupled with great Apache hadoop, Apache Spark and Solr integration and a strong tools ecosystem(unit tests, stress testing), it is a unbeatable combination!
  • As a hybrid architecture based on masterless architecture as in DynamoDB and column family data model as in BigTable, it hits the bulls eye!
  • It has best in class performance across different kinds of read/write/mixed workloads. It provides linear scalability which works for the best performance, lowest latency and highest throughput.
  • Being a tunable consistency model enables you to have consistency as your platform/application needs.
  • If configured correctly, there is no downtime and no data loss.These are key criterias on critical domains.
  • Apache Cassandra is lacking in some features, which Datastax provides in the Enterprise version. For example, security and advanced tools like OpsCenter. These would be a great addition to open source Apache Cassandra.
  • At times we noticed some versions had issues not known in advance, for example, LostNotificationError on repair of nodes. However steadily the newer releases have become better and more stable.
  • Examples of datastax native driver with Cassandra 2.1 can be improved, as it does not provide all scenarios one would need on production.
  • If you prefer to work with an open source project and be hands on, Apache Cassandra is one of the best. However if you need a managed cassandra like service where you do not even want to configure/deploy/backup/restack, a DynamoDB service would be more preferred.
  • Cassandra is JVM based NoSQL, hence garbage collector tuning is a key aspect, Garbage collection in JDK 8 and G1GC garbage collector is better or configure ConcurrentMarkSweep(CMS) garbage collector in an optimum manner.

Apache Cassandra is a NoSQL database and well suited where you need highly available, linearly scalable, tunable consistency and high performance across varying workloads. It has worked well for our use cases, and I shared my experiences to use it effectively at the last Cassandra summit! http://bit.ly/1Ok56TK

It is a NoSQL database, finally you can tune it to be strongly consistent and successfully use it as such. However those are not usual patterns, as you negotiate on latency. It works well if you require that. If your use case needs strongly consistent environments with semantics of a relational database or if the use case needs a data warehouse, or if you need NoSQL with ACID transactions, Apache Cassandra may not be the optimum choice.

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David Prinzing | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
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Cassandra is the only database used by Algorithmic Ads. We use it for both real-time transactions and analytics. The primary application accessing Cassandra is a light-weight Java application that provides a RESTful web services API for all our other applications. The API is a focal point for integration and includes both business logic and data. The same API is used both internally and by our customers. We rely on Cassandra for its amazing performance, linear scalability, and continuous availability.
  • Continuous availability: as a fully distributed database (no master nodes), we can update nodes with rolling restarts and accommodate minor outages without impacting our customer services.
  • Linear scalability: for every unit of compute that you add, you get an equivalent unit of capacity. The same application can scale from a single developer's laptop to a web-scale service with billions of rows in a table.
  • Amazing performance: if you design your data model correctly, bearing in mind the queries you need to answer, you can get answers in milliseconds.
  • Time-series data: Cassandra excels at recording, processing, and retrieving time-series data. It's a simple matter to version everything and simply record what happens, rather than going back and editing things. Then, you can compute things from the recorded history.
  • Cassandra is a poor choice for implementing application queues.
  • NoSQL requires thinking differently, and can be challenging for people with strong relational database backgrounds to understand. The CQL language helps with this, but it pays to understand how the engine works under the hood. That said, the benefits outweigh the challenge of the learning curve!
  • Database compactions and anti-entropy repair can be burdensome on a busy cluster. Significant improvements have been made in recent versions, but it remains as an operational challenge.
Cassandra excels in a broad range of applications -- especially if you understand its data model and write your applications accordingly. It's an excellent choice for time-series data, and a poor choice for application queues. It performs the best if you can simply record history and compute from it, rather than going back and editing or deleting things a lot.
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Kalpesh Gada | TrustRadius Reviewer
October 16, 2015

Cassandra Rocks !!!

Score 9 out of 10
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Verified User
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We used Cassandra to store personalization data of our customers so that we can have this information available through the cluster. The primary advantage of Cassandra is the cluster configuration so that there is not a single point of failure. The writes are faster when you want to write data into the storage. We used it for storing data in JSON format which is used to store anything in JSON format. The data was always up to date and there was less latency when we read from the system. I would highly recommend using Cassandra so as to make a system more scalable and process requests faster.
  • Cassandra is highly scalable.
  • It provides the flexibility to store data in any format. You can add column family dynamically as need by the application.
  • One of the best noSQL solutions I've used so far.
  • A better UI access for reading the data.
  • More graphical information to understand how the data is being processed, system uptime/downtime, etc.
  • I used Cassandra-cli for running quries but it is not very helpful when it returns a lot of results. If there was some way to improve the user queries, it would be great.
I think Cassandra is well suited when we want to store general data that is not really about banking transactions. There is a learning curve involved on how the data is stored and how it is processed.
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Gary Ogasawara | TrustRadius Reviewer
Score 9 out of 10
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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.
  • 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.
  • 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.
Well suited for multiple data centers, large networks, heterogeneous hardware.
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Anonymous | TrustRadius Reviewer
Score 9 out of 10
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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.
  • 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
  • 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.
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.

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Anonymous | TrustRadius Reviewer
Score 6 out of 10
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Verified User
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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.
  • 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.
  • 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.
Well Suited
Tunable Consistency
Write Speed

Less Appropriate
Ad-Hoc Queries
Unpredictable Performance
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Anonymous | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
Cassandra is being used as a time series store for sensor data and is used by several researchers within our department.
It serves as the storage layer in our home grown sensor analytics platforms that utilizes spark for the computation. We use it to store billions of samples of wearable sensor data that is collected in various studies and experiments.
  • High Availability - we utilize the data replication features of Cassandra. This enables us to access our data even when several nodes have gone down
  • Data Locality - our architecture combines Cassandra storage nodes and computation nodes in the same machine. This enables us to utilize data locality and limit expensive network IO to read data.
  • Elasticity - Cassandra is a shared nothing architecture. Nodes can be added very easily and they discover the network topology. As soon as a node has joined the Cassandra ring, the data is redistributed among the existing nodes and streamed to it automatically.
  • Cassandra runs on the JVM and therefor may require a lot of GC tuning for read/write intensive applications.
  • Requires manual periodic maintenance - for example it is recommended to run a cleanup on a regular basis.
  • There are a lot of knobs and buttons to configure the system. For many cases the default configuration will be sufficient, but if its not - you will need significant ramp up on the inner workings of Cassandra in order to effectively tune it.
Cassandra has excellent high availability and partition tolerance and has a robust architecture.
It is well suited for storing immutable data as deletes are extremely inefficient. As such, it is well suited for data archive and deep storage.
It is less appropriate for OLAP as has limited aggregation and filtering abilities, and no grouping whatsoever.
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Feature Scorecard Summary

Performance (5)
8.2
Availability (5)
8.6
Concurrency (5)
7.8
Security (5)
8.0
Scalability (5)
9.2
Data model flexibility (5)
6.6
Deployment model flexibility (5)
7.0

About Cassandra

Cassandra is a no-SQL database from Apache.
Categories:  NoSQL Databases

Cassandra Technical Details

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