While many of our applications use open-source systems like Hadoop, HBase, Cassandra, Mongo, RabbitMQ, and MySQL, our usage is fairly standard, but there is one aspect of what we do that is pretty unique. We collect or receive information from 100+ sources and we struggled early on to find a way to deal with how data from those sources changed over time, and we ultimately decided that we needed a data storage solution that could represent those changes. Basically, we needed to be able …
…machines without having to spend more money.
Compression means serving more data out of RAM, which means clients are happier because of the performance improvements.
The cost is higher CPU usage to perform the encrypt/decrypt. But disk IO is orders of magnitude slower than decompression and most servers have CPU to burn.
Edward's article is well written, has the specifics on how to turn on compression for Cassandra, pretty graphs, and lots more details.
Working towards arelease for 5.5 (currently in beta)
Additionally, we are actively working on expanding the supported Let us know!stack on Managed. Are you an Engine Yard Managed customer interested in or Cassandra?
Remember that you can always influence our roadmap by submitting data feature requests .
Data in Cassandra looks like a multi-level dict.
By default, Cassandra eats 1/2 of your RAM. You might want to change that ;)
He uses pycassa for his client. It's the simplest approach.
telephus is a Cassandra client for Twisted.
cassandra-dbapi2 is a Cassandra client that supports DBAPI2. It's based on Cassandra's new CQL interface.
Don't use pure Thrift to talk to Cassandra.
Cassandra is good about scaling up linearly.
There's a batch …
…to do as effectively in prose alone. the Cassandra storage engine at is a good example. Starting at about 22:00, he explains how Cassandra uses log-structured merge trees to turn random writes into sequential i/ o. Compare that with the treatment in the Bigtable paper , or the original 2012 LSTM paper . Sylvain's explanation is much more clear by virtue of how it's presented.'s talk on
I avoid audio or video during my …
…'s , 's , and Cassandra amongst others are all using a variant or a direct copy of this very architecture.
Simple on the surface, but as usual, implementation details matter a great deal. Thankfully, Jeff Dean and Sanjay Ghemawat , the original contributors to the and BigTable infrastructure at Google released , which is more or less an exact replica of the architecture we've … earlier last year
…he ported a relational database to threedata stores: , Cassandra and .
- Greg Unrein demonstrates how to make Openmix decisions with data.
- explains how to use 3.2 with 1.9.3 on .
- two-year plan for .shares their
- shares four things you should consider when selecting a cloud provider.
…Amazon has built all of the above on top of the basic Dynamo ingredients, Cassandra living proof that it's possible. But if Amazon did reuse a lot of the existing Dynamo code base, they hid it really well. All the evidence points to at least heavy usage of a sorted storage system under the covers, which works very well with SSDs, as they make sequential writes and reads nice and fast.
No matter what it is, Amazon has done something pretty great here. They hide most of the complexity …
…went to town. The result was a combination of Google Protocol Buffers, node.js, and Cassandra. Elegant, scalable, and totally unmaintainable.
"How many devices do we expect to have?"
"Well, we hope to sell 500 in 12 months."
"How often will they need to report in?"
Are future updates included?
Yes, as content gets added, typos get fixed, and new databases pop up, I'll send updates to everyone buying the book. The updates are free. Consider buying the book a subscription for more chapters on other databases.
Are you extending the book with more databases over time?
Yes, I have an insatiable thirst to …