@elvarb i did some measurements and I can't confirm that Elasticsearch uses much more space than InfluxDB. In fact, if you optimize the index (this should only be done on indices that are not receiving updates anymore), the consumed space by Elasticsearch is even way lower.
I've sent a week worth of datapoints (1 report/minute) to both InfluxDB and Elasticsearch.
Elasticsearch after optimize: 425.8M
Elasticsearch without optimize: 2.3G
Each second, 1000 Timers (16 datapoints) 2000 Meters (5 datapoints) and 3000 Gauges where reported. The test included 1008 reports, which is 23,184,000 datapoints in total.
The distribution of the different metric types (timers/meters/gauges) is derived from a real word monitoring setup with stagemonitor which had about 100 timers (16 datapoints/timer), 200 meters (5 datapoints/meter) and 300 gauges (1 datapoint/meter) = 2300 datapoints. If you set the stagemonitor reporting interval to 1 minute you get 23,184,000 datapoints per week (2300 * 60 * 24 * 7)
response_time,application=Metrics\ Store\ Benchmark,host=N51,instance=instance count=1,m1_rate=3.0,m5_rate=4.0,m15_rate=5.0,mean_rate=2.0,min=4.0,max=2.0,mean=4.0,median=6.0,std=5.0,p25=0.0,p75=7.0,p95=8.0,p98=9.0,p99=10.0,p999=11.0
meter,application=Metrics\ Store\ Benchmark,host=N51,instance=instance count=10,m1_rate=3.0,m5_rate=4.0,m15_rate=5.0,mean_rate=2.0
cpu_usage,application=Metrics\ Store\ Benchmark,host=N51,instance=instance value=1.0e-8
I've constantly updated the metrics with random values.
I've used the default settings and this index template: https://github.com/stagemonitor/stagemonitor/blob/influxdb/stagemonitor-core/src/main/resources/stagemonitor-elasticsearch-metrics-index-template.json
This is the benchmark: https://github.com/stagemonitor/stagemonitor/blob/influxdb/stagemonitor-core/src/test/java/org/stagemonitor/core/metrics/metrics2/MetricsStoreBenchmark.java
To execute it yourself, start an Elasticsearch and InfluxDB server and adjust the URLs.
If you're using an optimized mapping and optimize your indices, Elasticsearch is more storage efficient. I did not benchmark reading the data or how scalable the databases are, but the CERN paper seems to indicate that Elasticsearch is also better in this area.
So what is the advantage on InfluxDB then? As far as I can see it is only the better visualisation support -> Grafana (although Kibana 4 is nice as well) and things like continuous queries that you could use to downsample old data to reduce storage space.
But Elasticsearch seems to have better support for sophisticated queries/functions through aggregations (moving averages and hold-winters is in the pipeline for 2.0!). The biggest advantage I see is that it is much more mature and scaleable. InfluxDB's clustering API currently is in alpha state and you shouldn't form a cluster with more than 3 nodes. I doubt that they will ever catch up with Elasticsearch in this area. There is just so much more money and manpower behind Elastic. The relatively new project Beats by Elastic indicates that they will be investing in the timeseries/metrics area. I really hope that Grafana will at some point support Elasticsearch. Otherwise they might loose users that can't use Elasticsearch with it to Kibana.