In working on a data ingestion platform, one of the requirements is a low-latency real-time ML-related response using Kafka Streams.
Kafka is used as the message broker, and as of v0.10, Kafka Streams will be used in the place of something like Flink or Spark. Given the TICK stack, how would Telegraph fit in with Kafka Connect? The initial database to use was ElasticSearch, as another one of the requirements was to have text proximity search results and NLP. However, since that is a completely separate use-case from time-series data, we'll stick with the real-time requirement and a separate database for this use case: Aerospike InfluxDB OpenTSDB This guy at CERN http://cds.cern.ch/record/2011172/files/LHCb-TALK-2015-060.pdf determined that ElasticSearch was the clear winner over InfluxDB for time-series data. However reviewing Influx's results https://www.influxdata.com/influxdb-markedly-elasticsearch-in-time-series-data-metrics-benchmark/, it is the clear winner over ElasticSearch. I'm sure I could find other contradicting results for anything else (but really what was missed in the testing?). I would like to know from those who have actually implemented these systems what the experience-based winner is, more specifically, between Aerospike and InfluxDB. -- Remember to include the version number! --- You received this message because you are subscribed to the Google Groups "InfluxData" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/influxdb. To view this discussion on the web visit https://groups.google.com/d/msgid/influxdb/19f67d62-b3d4-4dd9-ad43-7d7edca124c1%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
