Apache Cassandra vs. Apache Geode

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.

Incentivized

The biggest advantage of using Apache Geode is DB like consistency. So for applications whose data needs to be in-memory, accessible at low latencies and most importantly writes have to be consistent, should use Apache Geode. For our application quite some amount of data is static which we store in MySQL as it can be easily manipulated. But since this data is large R/w from DB becomes expensive. So we started using Redis. Redis does a brilliant job, but with complex data structures and no query like capability, we have to manage it via code. We are experimenting with Apache Geode and it looks promising as now we can query on complex data-structures and get the required data quickly and also updates consistent.

Incentivized

• 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.

Incentivized

• Super Fast data pull/push
• Provided ACID transactions, so it works like a SQL Database
• Provides replication & partitioning, so our data is never lost and extraction is super fast. NoSql like properties

Incentivized

• 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.

Incentivized

• Needs more supporting languages. Out of box Python, Nodejs adapters would be wonderful
• Currently it supports just KV Store. But if we could cache documents or timeseries data would be great
• Needs more community support, documentation.

Incentivized

I would recommend Cassandra DB to those who know their use case very well, as well as know how they are going to store and retrieve data. If you need a guarantee in data storage and retrieval, and a DB that can be linearly grown by adding nodes across availability zones and regions, then this is the database you should choose.

Incentivized

It’s great tool but it can be complicated when it comes administration and maintenance.

Incentivized

Still Experimenting. Initial results are good. we need to figure out if we can completely replace Redis. Cost wise if it makes sense to keep both or replacement is feasible.

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Sometimes instead giving straight answer, we ‘re getting transfered to talk professional service.

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Never contacted support

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We evaluated MongoDB also, but don't like the single point failure possibility. The HBase coupled us too tightly to the Hadoop world while we prefer more technical flexibility. Also HBase is designed for "cold"/old historical data lake use cases and is not typically used for web and mobile applications due to its performance concern. Cassandra, by contrast, offers the availability and performance necessary for developing highly available applications. Furthermore, the Hadoop technology stack is typically deployed in a single location, while in the big international enterprise context, we demand the feasibility for deployment across countries and continents, hence finally we are favor of Cassandra

Incentivized

Still Experimenting. But looks promising as it has query capabilities over complex data structures

Incentivized

• I have no experience with this but from the blogs and news what I believe is that in businesses where there is high demand for scalability, Cassandra is a good choice to go for.
• Since it works on CQL, it is quite familiar with SQL in understanding therefore it does not prevent a new employee to start in learning and having the Cassandra experience at an industrial level.

Incentivized

• Still experimenting so difficult to quote
• For a small size project/teams might be an overkill as it still has certain learning curve
• For Medium to large projects with complex Data Structures that need to be queried with a fast o/p it definitely works

Incentivized