Usually, I’m trying to avoid comparisons with other databases. I can’t create objective benchmark because I can’t unlearn everything that I know about my own product. Most probably, I’ll create a biased benchmark that will work great with Akumuli. But fortunately, InfluxData released their own set of benchmarks and published series of articles comparing their product with the competition. I decided to give it a try.

There is a bunch of applications in this test suite (in /cmd dir). There is an app that generates input (one for all databases, you should tell it what database are you working with), the app that generates queries, the bunch of apps that used to load data (one per supported database), and another set of apps that can be used to benchmark each database (one per database as well) using generated queries.

An app that generates input is called bulk_data_gen . It can produce DevOps or IoT workload (the later haven’t been implemented yet, I think). The DevOps workload generator can produce input that simulates real-life DevOps workload. It contains metrics describing CPU, memory, Redis, Postgres and stuff like that. Each data point has a lot of tags and fields (up to 30+ fields). This is the first data point in InfluxDB format:

cpu,hostname=host_0,region=eu-west-1,datacenter=eu-west-1b,rack=67,os=Ubuntu16.10,arch=x86,team=NYC,service=7,service_version=0,service_environment=production usage_user=58.1317132304976170,usage_system=2.6224297271376256,usage_idle=24.9969495069947882,usage_nice=61.5854484633778867,usage_iowait=22.9481393231639395,usage_irq=63.6499207106198313,usage_softirq=6.4098777048301052,usage_steal=44.8799140503027445,usage_guest=80.5028770761136201,usage_guest_nice=38.2431182911542820 1451606400000000000

The table name is cpu . There is 10 tags - hostname , region , and so on, and also, there are 10 fields - usage_user , usage_system , etc.

What’s the problem with this? Many Time-Series databases don’t support fields (e.g. Graphite, OpenTSDB). With this databases, one should simulate fields using some series naming convention. E.g. for OpenTSDB bulk_data_gen app generates series names that contain table name and field name (cpu.usage_user, cpu.usage_nice, etc). This results in terrible protocol bloat, because each data point now should have entire set of tags and timestamp. The same data point in OpenTSDB protocol will look like this:

{ "metric" : "cpu.usage_user" , "timestamp" : 1451606400000 , "tags" :{ "arch" : "x86" , "datacenter" : "eu-west-1b" , "hostname" : "host_0" , "os" : "Ubuntu16.10" , "rack" : "67" , "region" : "eu-west-1" , "service" : "7" , "service_environment" : "production" , "service_version" : "0" , "team" : "NYC" }, "value" : 58.13171323049762 } { "metric" : "cpu.usage_system" , "timestamp" : 1451606400000 , "tags" :{ "arch" : "x86" , "datacenter" : "eu-west-1b" , "hostname" : "host_0" , "os" : "Ubuntu16.10" , "rack" : "67" , "region" : "eu-west-1" , "service" : "7" , "service_environment" : "production" , "service_version" : "0" , "team" : "NYC" }, "value" : 2.6224297271376256 } { "metric" : "cpu.usage_idle" , "timestamp" : 1451606400000 , "tags" :{ "arch" : "x86" , "datacenter" : "eu-west-1b" , "hostname" : "host_0" , "os" : "Ubuntu16.10" , "rack" : "67" , "region" : "eu-west-1" , "service" : "7" , "service_environment" : "production" , "service_version" : "0" , "team" : "NYC" }, "value" : 24.99694950699479 } { "metric" : "cpu.usage_nice" , "timestamp" : 1451606400000 , "tags" :{ "arch" : "x86" , "datacenter" : "eu-west-1b" , "hostname" : "host_0" , "os" : "Ubuntu16.10" , "rack" : "67" , "region" : "eu-west-1" , "service" : "7" , "service_environment" : "production" , "service_version" : "0" , "team" : "NYC" }, "value" : 61.58544846337789 } { "metric" : "cpu.usage_iowait" , "timestamp" : 1451606400000 , "tags" :{ "arch" : "x86" , "datacenter" : "eu-west-1b" , "hostname" : "host_0" , "os" : "Ubuntu16.10" , "rack" : "67" , "region" : "eu-west-1" , "service" : "7" , "service_environment" : "production" , "service_version" : "0" , "team" : "NYC" }, "value" : 22.94813932316394 } { "metric" : "cpu.usage_irq" , "timestamp" : 1451606400000 , "tags" :{ "arch" : "x86" , "datacenter" : "eu-west-1b" , "hostname" : "host_0" , "os" : "Ubuntu16.10" , "rack" : "67" , "region" : "eu-west-1" , "service" : "7" , "service_environment" : "production" , "service_version" : "0" , "team" : "NYC" }, "value" : 63.64992071061983 } { "metric" : "cpu.usage_softirq" , "timestamp" : 1451606400000 , "tags" :{ "arch" : "x86" , "datacenter" : "eu-west-1b" , "hostname" : "host_0" , "os" : "Ubuntu16.10" , "rack" : "67" , "region" : "eu-west-1" , "service" : "7" , "service_environment" : "production" , "service_version" : "0" , "team" : "NYC" }, "value" : 6.409877704830105 } { "metric" : "cpu.usage_steal" , "timestamp" : 1451606400000 , "tags" :{ "arch" : "x86" , "datacenter" : "eu-west-1b" , "hostname" : "host_0" , "os" : "Ubuntu16.10" , "rack" : "67" , "region" : "eu-west-1" , "service" : "7" , "service_environment" : "production" , "service_version" : "0" , "team" : "NYC" }, "value" : 44.879914050302744 } { "metric" : "cpu.usage_guest" , "timestamp" : 1451606400000 , "tags" :{ "arch" : "x86" , "datacenter" : "eu-west-1b" , "hostname" : "host_0" , "os" : "Ubuntu16.10" , "rack" : "67" , "region" : "eu-west-1" , "service" : "7" , "service_environment" : "production" , "service_version" : "0" , "team" : "NYC" }, "value" : 80.50287707611362 } { "metric" : "cpu.usage_guest_nice" , "timestamp" : 1451606400000 , "tags" :{ "arch" : "x86" , "datacenter" : "eu-west-1b" , "hostname" : "host_0" , "os" : "Ubuntu16.10" , "rack" : "67" , "region" : "eu-west-1" , "service" : "7" , "service_environment" : "production" , "service_version" : "0" , "team" : "NYC" }, "value" : 38.24311829115428 }

That’s a lot of text! And the cpu table is not the largest one! There is a table with 30+ fields and a huge set of tags. That’s why ingestion in OpenTSDB is slow (5x slower then InfluxDB) because database needs to parse input that large, no surprises here. We can see this in the article. But if your data don’t have all this fields figures will be different. I think that OpenTSDB wouldn’t look that bad.

But let’s look how Akumuli performs here. Akumuli doesn’t support fields and tables (there are only series names) but it has bulk load protocol that can encode many data points that share timestamp and tags using only one message. Because of that input size for Akumuli is about the same size as InfluxDB input (~1GB). I have run the tests on my machine and on two m4.xlarge AWS instances.

Write Performance

I’ve run the tests locally at first using InfluxDB v1.1.1 and latest version of Akumuli, here is the script output:

$ ./load_data.sh Loading data into Akumuli daemon URLs: [127.0.0.1:8282] loaded 19284480 items in 151.588062sec with 1 workers (mean rate 127216.350267/sec) Loading data into InfluxDB daemon URLs: [http://localhost:8086] [worker 0] backoffs took a total of 0.000000sec of runtime loaded 19284480 items in 423.174608sec with 1 workers (mean rate 45570.976222/sec, 19.99MB/sec from stdin)

Run time Write throughput Akumuli 151.6sec 1427650 points/sec InfluxDB 423sec 511407 points/sec

In the case of Akumuli ingestion speed was limited by the gzip decompression speed because the CPU core that has been running gzip was 100% busy. Actual disk write throughput was steady (iotop showed around 6M/s). InfluxDB has been writing to disk around 3-6 M/s with occasional bumps up to 13M/s and it was 33M/s one time. I guess this is due to compaction process.

I’ve used only one worker thread with both databases because I wasn’t able to replicate the real workload the other way. In real world, each connection will write to the unique subset of series. Akumuli is optimized for this case, it scales linearly with CPU cores and it actually uses many write threads to write data to disk. If this property doesn’t hold, this writer threads will need to synchronize access and this will limit throughput. Loaders from this test suite split messages amongst workers in round-robin manner but they should split them by hostname. That’s why I decided to use a single worker.

AWS

I tried to run this test on two m4.xlarge instances, one for database and another one to generate load. Results is here:

ubuntu@ip-172-31-41-230:~/work/src/influxdb-comparisons/cmd$ ./load_data.sh Loading data into Akumuli daemon URLs: [172.31.36.43:8282] loaded 19284480 items in 249.247543sec with 1 workers (mean rate 77370.792655/sec) Loading data into InfluxDB daemon URLs: [http://172.31.36.43:8086] [worker 0] backoffs took a total of 0.000000sec of runtime loaded 19284480 items in 614.613496sec with 1 workers (mean rate 31376.597036/sec, 13.76MB/sec from stdin)

As you can see, Akumuli is still faster. The throughput of both databases is smaller due to EBS volume being used instead of SSD.

On Disk Compression

InfluxDB did a great job here. As you can see it uses twice less disk space then Akumuli. It compresses this data almost as good as gzip does. This difference is due to compression algorithms being used, Akumuli uses a faster algorithm that avoids bit packing and InfluxDB uses a slower algorithm that uses bit packing and has a better compression ratio as result.

705M /home/elazin/.akumuli/ 323M /var/lib/influxdb/

Query Performance

I have run query benchmarks only locally. Before running each benchmark I cleaned file caches and then started the databases. The results are in the table below.

Akumuli Results

1 host, 12h by 1min 1 host, 1h by 1min 8 hosts, 1h by 1min 1 host, 12h by 1h min 1.59ms 0.70ms 1.93ms 0.77ms avg 1.93ms 0.82ms 2.22ms 0.88ms max 3.41ms 1.67ms 2.82ms 3.59ms

Here 1 host, 1h by 1min means that query reads 1 hour interval with 1 minute step for 1 host.

InfluxDB Results

1 host, 12h by 1min 1 host, 1h by 1min 8 hosts, 1h by 1min 1 host, 12h by 1h min 2.96ms 0.62ms 1.50ms 0.78ms avg 4.30ms 0.87ms 2.51ms 1.24ms max 11.02ms 5.62ms 9.89ms 6.26ms

As you can see, average performance is about the same (but Akumuli is just slightly faster). Both databases are column oriented, no surprises here. But max latency is quite different. I think that this is due to GC pauses in InfluxDB.

Final Thoughts

I tried to be objective but I don’t know InfluxDB very well so maybe I’m just using it incorrectly. But anyway, I’d like to encourage you to evaluate all products on your own. Probably, your use case is quite different from mine. If this is the case, you will see different results.