5 ms·

Do you have a source showing elasticache running faster than this? For example, Redis labs was only able to achieve 10M req/sec by using 6 m4.16xlarge instances

Do you have a source showing elasticache running faster than this? For example, Redis labs was only able to achieve 10M req/sec by using 6 m4.16xlarge instances which are double the price of the CPU instance we used: https://dzone.com/articles/10m-opssec-1msec-latency-with-only-6-ec2-nodes https://dzone.com/articles/10m-opssec-1msec-latency-with-onl... 100-500 byte values are the majority of requests at companies like Facebook and Lyft for their key value clusters. For large value sizes the network interface becomes the bottleneck so FPGAs won’t be able to help.

dormando 8y ago
I wrote the current version of OSS memcached. I don't know how elasticache is configured, but as I said memcached itself can definitely saturate the network from that instance. Either the version they run is too old or it's misconfigured. If I were to compare a "custom FPGA caching service" vs something memcached like, I would take the same 4xlarge instance and just run memcached on it. On a large enough machine I've gotten it up past 55 million read ops/sec. It's quite good at read throughput. I'm also familiar with the cache clusters at major companies.
- andrewcanis 8y ago
  Our assumption was that elasticache would be highly optimized by Amazon. Remember that these are virtual machines which means limitations such as packet per second throttling. What specific configuration options do you think are missing? In the latest version of memcached have you added support for batching/pipelining multiple requests per packet? Because this was crucial for achieving high requests/sec in this example. Were the 55M requests/sec coming from another machine? Even with small 100B values you would need a minimum of a 44 Gbps network link. How many cores were required? In our benchmark we wanted a fair comparison between instances of similar price and RAM size.
  - dormando 8y ago
    They stopped updating it a few years ago; it's probably also not as well tuned as you think. I'd need to see the output of "stats settings" from a running instance to know for sure. I also have no idea if it's a hacked fork or not. Odds are pretty good it's left at the default of 4 worker threads... so on a 16 vcpu instance that's not going to reach great heights. Since it's a 1.4.x version (years old), it's missing some newer features that both help in average latency and memory efficiency. Or rather, a lot of them are there but disabled by default. Memcached has allowed pipelining since it was created. For the ASCII protocol, packing multiple responses into single packets is done via a straight multiget. You can send multiple requests in a single packet for any protocol and any command. My stress utility (https://github.com/memcached/mc-crusher https://github.com/memcached/mc-crusher) has options for pipelining requests, and using multigets ascii packed get responses. I test to the limit of lock scaling for each individual subsystem. The 55M test required running mc-crusher via localhost, there's no network that can go that fast. My point is you're limited by the network throughput, not the CPU. In that particular 55M test, all cores were used, but ~7-8 of them were used by mc-crusher... so the real limit for the machine is even higher. It did have a lot of cores. 48ish? You can still do apples/apples with instance sizes... but given everything I know about this thing, unless those cores are extremely slow, hitting 11m ops/sec shouldn't be an issue. Or at least, with minimal fiddling it should hit 6-8m, which doesn't give you a crazy 9x figure. You do need to stop doing 1:1 get/set ratio though. Sets don't scale very well since I've generally never had complaints about the speed. I'd say a highly conservative test would be 5:1 get/set. Production workloads are typically even higher than that. (that said I do intend to speed them up more, it's just lowish priority.. the LRU locks are highly granular, so spreading sets across different slab classes can help mutation perf a lot).
    - andrewcanis 8y ago
      I'm still seeing similar results (~1M req/sec) after compiling your latest version of memcached from github and running with 16 worker threads. I just spun up two r4.4xlarge instances (one for client and one for the memcached server). I'm using memtier_benchmark with pipelining of 16 requests, 100B values, 10:1 get/set ratio. I compiled mc-crusher but you'll have to let me know the command to run because the readme wasn't clear. One main constraint here is that we are using AWS virtual machine instances on the cloud. My guess is your previous experience is with physical servers. The FPGA performance is also significantly better when you can use the physical board with a direct ethernet connection, pipelining isn't required in this case the FPGA can handle minimum sized ethernet packets at line rate. Another question, in your experience is compression/encryption used much with memcached? Because this is another area where the FPGA can compute much faster.

dormando 8y ago

I wrote the current version of OSS memcached. I don't know how elasticache is configured, but as I said memcached itself can definitely saturate the network from that instance. Either the version they run is too old or it's misconfigured. If I were to compare a "custom FPGA caching service" vs something memcached like, I would take the same 4xlarge instance and just run memcached on it. On a large enough machine I've gotten it up past 55 million read ops/sec. It's quite good at read throughput. I'm also familiar with the cache clusters at major companies.

andrewcanis 8y ago
Our assumption was that elasticache would be highly optimized by Amazon. Remember that these are virtual machines which means limitations such as packet per second throttling. What specific configuration options do you think are missing? In the latest version of memcached have you added support for batching/pipelining multiple requests per packet? Because this was crucial for achieving high requests/sec in this example. Were the 55M requests/sec coming from another machine? Even with small 100B values you would need a minimum of a 44 Gbps network link. How many cores were required? In our benchmark we wanted a fair comparison between instances of similar price and RAM size.
- dormando 8y ago
  They stopped updating it a few years ago; it's probably also not as well tuned as you think. I'd need to see the output of "stats settings" from a running instance to know for sure. I also have no idea if it's a hacked fork or not. Odds are pretty good it's left at the default of 4 worker threads... so on a 16 vcpu instance that's not going to reach great heights. Since it's a 1.4.x version (years old), it's missing some newer features that both help in average latency and memory efficiency. Or rather, a lot of them are there but disabled by default. Memcached has allowed pipelining since it was created. For the ASCII protocol, packing multiple responses into single packets is done via a straight multiget. You can send multiple requests in a single packet for any protocol and any command. My stress utility (https://github.com/memcached/mc-crusher https://github.com/memcached/mc-crusher) has options for pipelining requests, and using multigets ascii packed get responses. I test to the limit of lock scaling for each individual subsystem. The 55M test required running mc-crusher via localhost, there's no network that can go that fast. My point is you're limited by the network throughput, not the CPU. In that particular 55M test, all cores were used, but ~7-8 of them were used by mc-crusher... so the real limit for the machine is even higher. It did have a lot of cores. 48ish? You can still do apples/apples with instance sizes... but given everything I know about this thing, unless those cores are extremely slow, hitting 11m ops/sec shouldn't be an issue. Or at least, with minimal fiddling it should hit 6-8m, which doesn't give you a crazy 9x figure. You do need to stop doing 1:1 get/set ratio though. Sets don't scale very well since I've generally never had complaints about the speed. I'd say a highly conservative test would be 5:1 get/set. Production workloads are typically even higher than that. (that said I do intend to speed them up more, it's just lowish priority.. the LRU locks are highly granular, so spreading sets across different slab classes can help mutation perf a lot).
  - andrewcanis 8y ago
    I'm still seeing similar results (~1M req/sec) after compiling your latest version of memcached from github and running with 16 worker threads. I just spun up two r4.4xlarge instances (one for client and one for the memcached server). I'm using memtier_benchmark with pipelining of 16 requests, 100B values, 10:1 get/set ratio. I compiled mc-crusher but you'll have to let me know the command to run because the readme wasn't clear. One main constraint here is that we are using AWS virtual machine instances on the cloud. My guess is your previous experience is with physical servers. The FPGA performance is also significantly better when you can use the physical board with a direct ethernet connection, pipelining isn't required in this case the FPGA can handle minimum sized ethernet packets at line rate. Another question, in your experience is compression/encryption used much with memcached? Because this is another area where the FPGA can compute much faster.