Would Dgraph be a useful tool for analyzing customer UI behavior over time (seconds, minutes, hrs, etc)? We do a lot of pathing analysis at a granular level for customer sessions as well as long term trends in user behavior. Today we use a lot of tabular data, mostly in SQL (Redshift) and some Pandas/SparkDF type environments. This setup is really taxing, given we’re managing the time series component, as well as user/item level metadata.
Dgraph can be used to store and filter using timeseries data, yes. There is a schema type called
dateTime. I’d say load small amount of data and play around with the query language to see if it helps you achieve what you are looking for.
Thank you for your reply. I was wondering if you would be able to shed light on the implications of the design of Dgraph and its usage in OLAP (online analytics processing) scenarios with traditional RDBMS and Columnar or Time-series Database (such as Prometheus), given that one of Dgraph’s aims was to be a replacement for traditional Relational Databases.
Dgraph can perform arbitrarily deep nested complex queries (see variable support) which makes it suited for OLAP. Given how the sytem is designed doing JOINS is cheap and fast.
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