Postgres and other musings

Announcing TPC-C.js; a Lightweight Implementation of TPC-C

I am glad to announce the beta release of TPC-C.js, which implements one of the most popular database benchmarks, TPC-C. It’s not a coincidence that today is also the 22nd anniversary of the TPC-C benchmark.

It currently supports Postgres database, but can be easily extended to test other database systems.

You might ask “Why another TPC-C implementation when we already have so many of them?”“

Short answer: This one is very light on system resources, so you can

  1. Run the benchmark strictly adhering to the specification, and
  2. Invest more in database hardware, rather than client hardware.

Long answer: It’s covered in the Motivation section of TPC-C.js, which I’ll quote here:


The TPC-C benchmark drivers currently available to us, like TPCC-UVa, DBT2, HammerDB, BenchmarkSQL, etc., all run one process (or thread) per simulated client. Because the TPC-C benchmark specification limits the max tpmC metric (transactions per minute of benchmark-C) from any single client to be 1.286 tpmC, this means that to get a result of, say, 1 million tpmC we have to run about 833,000 clients. Even for a decent number as low as 100,000 tpmC, one has to run 83,000 clients.

Given that running a process/thread, even on modern operating systems, is a bit expensive, it requires a big upfront investment in hardware to run the thousands of clients required for driving a decent tpmC number. For example, the current TPC-C record holder had to run 6.8 million clients to achieve 8.55 million tpmC, and they used 16 high-end servers to run these clients, which cost them about $ 220,000 (plus $ 550,000 in client-side software).

So, to avoid those high costs, these existing open-source implementations of TPC-C compromise on the one of the core requirements of the TPC-C benchmark: keying and thinking times. These implementations resort to just hammering the SUT (system under test) with a constant barrage of transactions from a few clients (ranging from 10-50).

So you can see that even though a decent modern database (running on a single machine) can serve a few hundred clients simultaneously, it ends up serving very few (10-50) clients. I strongly believe that this way the database is not being tested to its full capacity; at least not as the TPC-C specification intended.

The web-servers of yesteryears also suffer from the same problem; using one process for each client request prohibits them from scaling, because the underlying operating system cannot run thousands of processes efficiently. The web-servers solved this problem (known as c10k problem) by using event-driven architecture which is capable of handling thousands of clients using a single process, and with minimal effort on the operating system’s part.

So this implementation of TPC-C uses a similar architecture and uses NodeJS, the event-driven architecture, to run thousands of clients against a database.