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I search an alternative to ELK, and the database backend should be based on PostgreSQL.

Required features:

  • open source
  • Web-GUI for performing queries. This is the K of ELK: Kibana
  • Based on PostgreSQL: E of ELK: Elasticsearch
  • L of ELK (Logstash): Does not matter in this context.
  • We will need much more information to give good recommendations here – asking for "a tool like X" is never giving enough details, even if linked. You should always list your requirements explicitly. Please see How to ask for an alternative to some software and the questions linked to it for details. Consider people never heard of ELK, but working with exactly that alternative you're looking for: What must the software achieve? – Izzy Feb 21 '17 at 13:51
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I'm very excited about TimescaleDB, which is an extension to Postgres that creates hyper tables that handle all the time series partitioning magic and supposedly bring huge scale to write throughput. We're going to try a POC with this:

https://www.timescale.com

Here are some relevant links to frame the discussion of "Can relational databases be used for time series data"

https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/63950

https://blog.timescale.com/time-series-data-postgresql-10-vs-timescaledb-816ee808bac5

https://blog.timescale.com/tutorial-installing-timescaledb-on-aws-c8602b767a98

https://blog.timescale.com/choose-postgresql-for-iot-19688efc60ca

http://www.timescale.com/papers/timescaledb.pdf

Scroll to slides. Wow https://www.percona.com/live/17/sessions/building-scalable-time-series-database-postgresql

  • I ask myself what are the benefits? If I understood the docs correctly, it is an optimized version of postgres. Up to now we don't have performance problems, since we don't use postgres for timeseries. We don't use PG up to now, since only few ELK like software exists for it. I hope timescale will convince more and more people to use postgres for use cases where ELK gets used today. One thing is sure: The future will look different :-) – guettli Nov 27 '17 at 11:51
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I discovered redash

Here the top of the README

START README

Redash is our take on freeing the data within our company in a way that will better fit our culture and usage patterns.

Prior to Redash, we tried to use traditional BI suites and discovered a set of bloated, technically challenged and slow tools/flows. What we were looking for was a more hacker'ish way to look at data, so we built one.

Redash was built to allow fast and easy access to billions of records, that we process and collect using Amazon Redshift ("petabyte scale data warehouse" that "speaks" PostgreSQL). Today Redash has support for querying multiple databases, including: Redshift, Google BigQuery, PostgreSQL, MySQL, Graphite, Presto, Google Spreadsheets, Cloudera Impala, Hive and custom scripts.

Redash consists of two parts:

Query Editor: think of JS Fiddle for SQL queries. It's your way to share data in the organization in an open way, by sharing both the dataset and the query that generated it. This way everyone can peer review not only the resulting dataset but also the process that generated it. Also it's possible to fork it and generate new datasets and reach new insights.

Dashboards/Visualizations: once you have a dataset, you can create different visualizations out of it, and then combine several visualizations into a single dashboard. Currently it supports charts, pivot table and cohorts.

END README

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