Numbers every programmer should know and their impact on benchmarks
Disclaimer: I don’t mean to be picking on the particular organizations / projects / people who I’ll mention below. They are just examples of a larger trend I observed.
Sometimes (most of the times?) we forget just how powerful the machines in our pockets / bags / desks are and accept the inefficiencies of the software running on them. When we start to celebrate those inefficiencies, a line has to be drawn though. Two examples:
In 2013 Twitter claimed a record Tweets Per Second (TPS – cute :-)) of ~143k. Lets round that up to 150k and do some back-of-the envelope calculations:
- Communication between the clients and Twitter: a tweet is 140 bytes (240 if we allow for unicode). Lets multiple the 150k number by 10 (just to be generous – remember that 143k was already a big blip) – we get a bandwidth requirement of 343 MB/sec. Because tweets are going over TCP presumably and ~20% of a TCP connection is overhead, you would need 428 MB/s of bandwidth, about 3.5 gigabit or less than 0.5 of a 10 gigabit connection.
- On the backend: lets assume we want triple redundancy (1 master + 2 replica) and that the average tweet goes out to 9 subscribers. This means that internally we need to write each tweet 30 times (we suppose a completely denormalized structure, we need to write the tweet to the users timeline also and do all this thrice for redundancy). This means 10 GB/sec of data (13 if we’re sending it over the network using TCP).
- Thus ~100 servers would be able to easily handle the load. And remember this is 10x of the peak traffic they experienced.
So why do the have 20 to 40 times that many servers? This means that less than 10% (!) of their server capacity is actually used for business functions.
Second example: Google with DataStax came out with a blogpost about benchmarking a 300 node Cassandra cluster on Google Compute Engine. They claim a peak of 1.2M messages per second. Again, lets do some calculations:
- The messages were 170 bytes in size. They were written to 2+1 nodes which would mean ~600 MB/s of traffic (730 MB/s if over the network using TCP).
- They used 300 servers but were also testing the resiliency by removing 1/3 of the nodes, so lets be generous and say that the volume was divided over 100 servers.
This means that per server we use 7.3 MB/s network traffic and 6 MB/s disk traffic or 6% or a Gigabit connection and about 50% of medium quality spinning rust HDD.
My challenge to you is: next time you see such benchmarks do a quick back-of-the envelope calculation and if it uses less than 60% of the available throughput, call the people on it!