sergei
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http://sergeitsar.blogspot.com
http://www.clustrix.com
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Comment · 13 years ago
Several problems here:
1. Unlike your dataset, the tpcc dataset for the benchmark was not memory resident. Total dataset size was 786G. Just shrinking the dataset to fit in memory would substantially change the numbers.
2. The tpcc workload is much more complex than your benchmark. It doesn't make sense to compare a tpcc transaction, of which there are multiple flavors, to your workload.
3. All Clustrix write transactions are multi-node transactions. There's a 2x data redundancy in the test. We do not have a master-slave model for data distribution. Docs on our data distribution model: http://docs.clustrix.com/display/CLXDOC/Data+Distribution.
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Now we've also done tests where the workload is a simple point updates and select on memory resident datasets. For those kinds of workloads, a 20 node cluster can do over 1M TPS.
Comment · 13 years ago
What do you base your performance claim vs Clustrix on?
Comment · 13 years ago
I frequently see apps with thousands of distinct query signatures. Having a developer manually chose indexes, join ordering, aggregation method, (just to name a few) for every single query, and then select multiple plans because input parameters absolutely do result in scenario where plans can be 10,000x off in performance -- well, that's just untenable.
Not trying to claim that CBOs are the panacea here, but let's be realistic. Having developers manually plan every single query is not the right choice.
Comment · 13 years ago
In practice, you end up with apps which dynamically build up predicates in different sections of the code. And when you combine that with many predicates, many tables, and other constraints such as ordering or aggregates, things get complex pretty quick.
Even if you have the best developers who understand all the in an outs of the dataset, re-implementing an optimizer in the app is rarely the right choice.
Comment · 13 years ago
A decent cost based optimizer will be right most of the time, and you have hints for the cases where it falters.
Your approach is simply hiding the query optimizer choices inside of the app, making it more fragile.
Let's say I have two fields: a and b, both of which have an index. And then I have a query with predicates across both a and b. For example, a = 10 and b = 20.
One of the core facilities within an optimizer is selectivity estimation. By looking at the statistics, the optimizer will see that a = 10 might look at 10,000 rows while b = 20 might only look at 10. So the optimal and desired choice would be to use index over b.
However, the exact same query construct with different parameters (e.g. a = 50 and b = 3) might flip the index selection.
Now let's imagine I had to implement this inside of my app. Every time I have such a constraint, the app has to become aware of selectivity to know which indexes to use based on input parameters.
Comment · 13 years ago
Check out a previous comment thread on the subject:
https://news.ycombinator.com/item?id=5068489
Comment · 13 years ago
Check out a previous comment thread on the subject:
Comment · 14 years ago
Yes, but they are in-memory only. I think the largest database they support is 50GB.
It's the kind of solution that works really well if you have key-value store problem where you want really low latencies. Telco call session state and session state for gaming comes to mind.
But beyond those use cases, you start running into a lot of architectural limitations...
Comment · 14 years ago
1) Both joins and aggregates are processed in parallel. In short, the larger your cluster, the faster we evaluate a complex query because we can bring more hardware to the problem.
You can get a better idea of how we scale distributed joins from one of my blog posts:
http://www.clustrix.com/blog/bid/242790/Scaling-Distributed-...
Once we complete the join, we will also perform the aggregate SUM operation across multiple nodes, getting individual sums, and then forwarding the results to another node for a final tally.
As a rule of thumb, each node has the throughput of about 70% of MySQL on the same hardware. So if you have a complex query that can take advantage of the cluster (100k rows per table examined qualifies), then you will see a 10x speedup on a 16 node cluster over a single instance.
However, that assumes that your entire data set fits in cache on the single instance box. As soon as that's no longer true, it's more likely that you will see a 100x or more speedup from Clustrix because you will have way more memory available for the buffer cache.
For example, if you have a 32GB single instance MySQL server, but you have more than 64GB of working set size, you're going to be limited by IO.
On Clustrix, if you move this to say a 4 node cluster, you are going to have an effective cache footprint of 128GB. So Clustrix will operate entirely out of cache.
2) We're fully ACID compliant using a two phase commit w/ PAXOS for transaction resolution. Because we're MVCC, readers never have to take out locks. To coordinate WRITE access to the same data, we have a row level distributed lock manager. That means that the entire cluster participates in lock management.
So yes, this has a higher communication overhead over a single server. If you have a small write workload with very little concurrency, you will see better query latencies from a single instance database.
However, as soon as you start getting to more serious write workloads with higher concurrency, Clustrix will distribute your writes across several machines in the cluster. We can handle an aggregate write throughput you can't touch with a single instance database, even with a fancy/expensive storage array.
One of our customers, for example, has a peak throughput of 70,000 transactions/sec. They're about 90/10 read/write, so that breaks down to 10,000 TPS for writes. And they are really fond of multi-row updates, insert .. ignore, insert .. on dup update kind of queries. So it's not exactly a lightweight key-value application. Prior to Clustrix, their MySQL installation peaked out at 5,500 TPS total (read+write). They now have a 15 node Clustrix cluster.
It's true, you can always construct a workload that will not distribute well. But these are generally rare degenerate cases that stem from poor application design, and would perform equally poorly on a single instance database.
Comment · 14 years ago
It's a good question. We built the entire database from the ground up, without any MySQL code. So while we support most of the frequently used features, we don't support everything.
You can get a list of unsupported features and differences from our docs:
http://docs.clustrix.com/display/CLXDOC/Unsupported+Features
http://docs.clustrix.com/display/CLXDOC/Feature+Differences
Having said that, our customers find that the value that they get out of scale out, fault tolerance, and performance is worth the tradeoffs of not supporting every single MySQL feature.
Honestly, our goal is not to be 100% MySQL compatible. We're aiming to be compatible enough to capture a significant share of existing MySQL installs, but we're really after building a truly scalable relational db.
Comment · 14 years ago
Try it. You'll like it :)
Comment · 14 years ago
It's different from a typical sharding approach (including what MongoDB does). In their model, you take a single key and distribute your data using that key (e.g. user_id). The problem surfaces when you look at secondary indexes.
If you have a secondary index say on user_location, and you want to query by that index, you don't know which shard to go to. So you end up broadcasting.
Another problem is enforcing unique index constraints.
With Clustrix, every table and index gets its own distribution.
So if you have a schema like this:
foo(a, b, c, d) unique idx1(b,c) idx2(d)
Clustrix treats each table and index as a different distribution. So if I need to look something up by d, I know exactly which node has the data. I can also enforce index uniqueness.
Comment · 14 years ago
Some reading material on the distributed approach:
http://www.clustrix.com/blog/bid/242790/Scaling-Distributed-...
http://www.clustrix.com/Portals/146389/docs/clustrix_a_new_a...