Wide events is a great concept for observability space! This a superset of structured logs and traces. Wide events is basically structured logs, where every log

Wide events is a great concept for observability space! This a superset of structured logs and traces. Wide events is basically structured logs, where every log entry contains hundreds of fields with various properties of the log entry. This allows slicing and dicing the collected events by arbitrary subsets of thier fields. This opens an infinite possibilities to obtain useful analytics from the collected events. Wide events can be stored in traditional databases. But this approach has a few drawbacks: - Every wide event can have different sets of fields. Such fields cannot be mapped to the classical relational table columns, since the full set of potential fields, which can be seen in wide events, isn't known beforehand. - The number of fields in wide events is usually quite big - from tens to a few hundreds. If we are going to store them in a traditional relational table, this table will end up with hundreds of columns. Such tables aren't processed efficiently by traditional databases. - Typical queries over wide events usually refer only a few fields out of hundreds of available fields. Traditional databases usually store every row in a table as a contiguous chunk of data with all the values for all the fields of the row (aka row-based storage). Such a scheme is very inefficient when the query needs to process only a few fields out of hundreds of available fields, since the database needs to read all the hundreds fields per each row and then extract the needed few fields. It is much better to use analytical databases such as ClickHouse for storing and processing of big volumes of wide events. Such databases usually store values per every field in contiguous data chunks (aka column-oriented storage). This allows reading and processing only the needed few fields mentioned in the query, while skipping the rest of hundreds fields. This also allows efficiently compressing field values, which reduces storage space usage and improves performance for queries limited by disk read speed. Analytical databases don't resolve the first issue mentioned above, since they usually need creating a table with the pre-defined columns before storing wide events into it. This means that you cannot store wide events with arbitrary sets of fields, which can be unknown before creating the table. I'm working on a specialized open-source database for wide events, which resolves all the issues mentioned above. It doesn't need creating any table schemas before starting ingesting wide events with arbitrary sets of fields (e.g. it is schemaless). It automatically creates the needed columns for all the fields it sees during data ingestion. It uses column-oriented storage, so it provides query performance comparable to analytical databases. The name of this database is VictoriaLogs. Strange name for the database specialized for efficient processing of wide events :) This is because initially it was designed for storing logs - both plaintext and structured. Later it has been appeared that it's architecture ideally fits wide events. Check it out - https://docs.victoriametrics.com/victorialogs/ https://docs.victoriametrics.com/victorialogs/