Benchmarks of Cassandra, HBase, VoltDB, MySql, Voldemort and Redis
vldb.org
vldb.org
A limited number of synchronous loads are always going to present scalability problems and will do a poor job measureing the throughput achievable. Even if it takes 500usecs to round-trip a transaction from the client, that means each synchronous client can only do 2000 per second. To scale linearly to 12 nodes might require hundreds of these synchronous clients.
VoltDB is commonly used with many parallel synchronous clients, such as web front-ends, or asynchronous workloads, such as event feeds. Both of these workload scale very well, so long as there is enough parallelism.
They also used global, consistent, multi-partition transactions for the YCSB scan. I'm not sure if that is necessary, and it would also limit scalability.
I can't speak too directly to the work because the code and configuration doesn't seem to be public. Perhaps I'm mistaken.
It would have been nice if they had reached out to us at VoltDB to verify their configuration and client. They also could have contacted Andy Pavlo at the H-Store who has some experience with YCSB on H-Store.
Still, we can always learn from something like this. One thing we're working on in VoltDB is better performance for workloads that aren't designed to be parallel. This means more performance for synchronous clients and for clients that do global consistent reads. Another area we could improve is more prominent diagnostics to show you why your cluster isn't running faster. That information is all available in our system tables and management tools, but we could probably boil it down to a single status field telling you whether your cluster is starved on client requests, intercluster-transaction-agreement or slow procedures. In our experience, it's usually the first one. I'll go file a ticket now.
Short answer, Volt establishes a global order across transactions. Transactions can't be executed immediately because their position in the global order isn't known when they initially arrive. The exchange of ordering information is driven by in-flight transactions. In the absence of sufficient in-flight transactions to drive the ordering process heartbeats are sent every 5 milliseconds resulting in increased latency and lower throughput as the system waits on heartbeats. 2-4x the concurrency should have shown different results.
We're finishing a different transaction initiation system that doesn't produce a global order so that people can do this kind of benchmark and get the expected result. You can switch the beta version on in 2.8.
I also wonder if they had clock skew. The global ordering process is always delayed by the amount of clock skew you have. With NTP properly configured this is 10s of microseconds, but with a typical out of the box NTP config it will be milliseconds unless you have a lot of uptime.
I don't really see this as an excuse and it is why we are eating the pain of a transaction initiation rewrite so people can use Volt the traditional way with small numbers of synchronous client threads.
This is something we address in our docs, but people naively testing VoltDB run into it more than we'd like. So for the next major release, we've re-worked how we do global ordering and can now achieve sub-millisecond latency on larger clusters in internal testing. Note that there's no change to our serializable consistency to achieve lower latency. So this new scheme has a huge impact on synchronous workloads, but the scalability with enough parallelism has been close to linear all along.
Sure, it's possible to determine which one has the best land speed, range, and fuel economy, but you're also sure as heck not going to use a Gillig Advantage to do an F-150's job or vice versa.
Disclaimer: HBase developer here.
If you think at it, in a real-world scenario if you have 100 database nodes, you likely also have not threaded clients, but N different processes running in M different computer systems, querying different nodes independently.
Everyone who's ever written a hello world benchmark? Learn from this.
This makes the decision of Facebook to go with HBase for their new messaging platform back in 2010 all the more strange. Though that was two years ago so things might have changed in Cassandra's favor since then.
Speed isn't everything to a database. AFAIK they chose HBase over Cassandra because of consistency guarantees: eventual consistency is a bad choice for a messaging platform.
Strange you would mention that in the context of Cassandra, since it allows for per-read/write configuration of consistency, from "eventual" to "strong". You get exactly what you ask for with Cassandra, whether its availability or consistency.
AFAIK, HBase only supports strong consistency.
http://rubyscale.com/blog/2011/03/06/basic-time-series-with-...
http://www.datastax.com/dev/blog/advanced-time-series-with-c...
This paper does not reflect what I have seen in production setups.
Disclaimer: HBase developer here.
I just glanced over this, but at this point they might have very well benchmarked driver performance. I also don't see why somebody would compare Cassandra against MySQL and against Redis.
While they all persist application data, their feature sets are so completely different that I don't see for which task people would have to decide between them.
Either you want to scale over multiple systems or you don't. Either you have more data than RAM or you don't.
Many discussion forums are SQL backed - but both Reddit and Digg are reported to use Cassandra, a NoSQL system.
Would anyone really use Redis for 'big data' ?
Sorry, boys, but this paper looks like the result of a ill performed research job, done in a hurry of getting published.