Cassandra cluster - data density (data size per node) - looking for feedback and advises

Question

I am considering the design of a Cassandra cluster.

The use case would be storing large rows of tiny samples for time series data (using KairosDB), data will be almost i

Answer 1

I haven't used KairosDB, but if it gives you some control over how Cassandra is used, you could look into a few things:

1. See if you can use incremental repairs instead of full repairs. Since your data is an immutable time series, you won't often need to repair old SSTables, so incremental repairs would just repair recent data.

2. Archive old data in a different keyspace, and only repair that keyspace infrequently such as when there is a topology change. For routine repairs, only repair the "hot" keyspace you use for recent data.

3. Experiment with using a different compaction strategy, perhaps DateTiered. This might reduce the amount of time spent on compaction since it would spend less time compacting old data.

4. There are other repair options that might help, for example I've found the the -local option speeds up repairs significantly if you are running multiple data centers. Or perhaps you could run limited repairs more frequently rather than performance killing full repairs on everything.

I have some Cassandra clusters that use RAID5. This has worked fine so far, but if two disks in the array fail then the node becomes unusable since writes to the array are disabled. Then someone must manually intervene to fix the failed disks or remove the node from the cluster. If you have a lot of nodes, then disk failures will be a fairly common occurrence.

If no one gives you an answer about running 20 TB nodes, I'd suggest running some experiments on your own dataset. Set up a single 20 TB node and fill it with your data. As you fill it, monitor the write throughput and see if there are intolerable drops in throughput when compactions happen, and at how many TB it becomes intolerable. Then have an empty 20 TB node join the cluster and run a full repair on the new node and see how long it takes to migrate its half of the dataset to it. This would give you an idea of how long it would take to replace a failed node in your cluster.

Hope that helps.

Answer 2

The risk of super dense nodes isn't necessarily maxing IO during repair and compaction - it's the inability to reliably resolve a total node failure. In your reply to Jim Meyer, you note that RAID5 is discouraged because the probability of failure during rebuild is too high - that same potential failure is the primary argument against super dense nodes.

In the days pre-vnodes, if you had a 20T node that died, and you had to restore it, you'd have to stream 20T from the neighboring (2-4) nodes, which would max out all of those nodes, increase their likelihood of failure, and it would take (hours/days) to restore the down node. In that time, you're running with reduced redundancy, which is a likely risk if you value your data.

One of the reasons vnodes were appreciated by many people is that it distributes load across more neighbors - now, streaming operations to bootstrap your replacement node come from dozens of machines, spreading the load. However, you still have the fundamental problem: you have to get 20T of data onto the node without bootstrap failing. Streaming has long been more fragile than desired, and the odds of streaming 20T without failure on cloud networks are not fantastic (though again, it's getting better and better).

Can you run 20T nodes? Sure. But what's the point? Why not run 5 4T nodes - you get more redundancy, you can scale down the CPU/memory accordingly, and you don't have to worry about re-bootstrapping 20T all at once.

Our "dense" nodes are 4T GP2 EBS volumes with Cassandra 2.1.x (x >= 7 to avoid the OOMs in 2.1.5/6). We use a single volume, because while you suggest "cassandra now supports JBOD quite well", our experience is that relying on Cassandra's balancing algorithms is unlikely to give you quite what you think it will - IO will thundering herd between devices (overwhelm one, then overwhelm the next, and so on), they'll fill asymmetrically. That, to me, is a great argument against lots of small volumes - I'd rather just see consistent usage on a single volume.

Answer 3

I would recommend to think about the data model of your application and how to partition your data. For time series data it would probably make sense to use a composite key [1] which consists of a partition key + one or more columns. Partitions are distributed across multiple servers according to the hash of the partition key (depending on the Cassandra Partitioner that you use, see cassandra.yaml).

For example, you could partition your server by device that generates the data (Pattern 1 in [2]) or by a period of time (e.g., per day) as shown in Pattern 2 in [2].

You should also be aware that the max number of values per partition is limited to 2 billion [3]. So, partitioning is highly recommended. Don't store your entire time series on a single Cassandra node in a single partition.

[1] http://www.planetcassandra.org/blog/composite-keys-in-apache-cassandra/

[2] https://academy.datastax.com/demos/getting-started-time-series-data-modeling

[3] http://wiki.apache.org/cassandra/CassandraLimitations