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Frequently asked questions

How can I improve the speed?

In most cases the bottlenecks are look-up experts. In these cases you can easily use the integrated load balancing features.


When using the AMQP broker, you can make use of Multi-threading. See the multithreading section.

"Classic" load-balancing (Multiprocessing)

Before Multithreading was available in IntelMQ, and in case you use Redis as broker, the only way to do load balancing involves more work. Create multiple instances of the same bot and connect them all to the same source and destination bots. Then set the parameter load_balance to true for the bot which sends the messages to the duplicated bot. Then, the bot sends messages to only one of the destination queues and not to all of them.

True Multiprocessing is not available in IntelMQ. See also this discussion on a possible enhanced load balancing <186>.

Other options

For any bottleneck based on (online) lookups, optimize the lookup itself and if possible use local databases.

It is also possible to use multiple servers to spread the workload. To get the messages from one system to the other you can either directly connect to the other's pipeline or use a fast exchange mechanism such as the TCP Collector/Output (make sure to secure the network by other means).

Removing raw data for higher performance and less space usage

If you do not need the raw data, you can safely remove it. For events (after parsers), it keeps the original data, eg. a line of a CSV file. In reports it keeps the actual data to be parsed, so don't delete the raw field in Reports - between collectors and parsers.

The raw data consumes about 50% - 30% of the messages' size. The size of course depends on how many additional data you add to it and how much data the report includes. Dropping it, will improve the speed as less data needs to be transferred and processed at each step.

In a bot

You can do this for example by using the Field Reducer Expert. The configuration could be:

  • type: blacklist
  • keys: raw

Other solutions are the Modify bot and the Sieve bot. The last one is a good choice if you already use it and you only need to add the command:

remove raw

In the database

In case you store data in the database and you want to keep its size small, you can (periodically) delete the raw data there.

To remove the raw data for a events table of a PostgreSQL database, you can use something like:

UPDATE events SET raw = NULL WHERE "time.source" < '2018-07-01';

If the database is big, make sure only update small parts of the database by using an appropriate WHERE clause. If you do not see any negative performance impact, you can increase the size of the chunks, otherwise the events in the output bot may queue up. The id column can also be used instead of the source's time.

Another way of reducing the raw-data from the database is described in the EventDB documentation: eventdb_raws_table.

How to Uninstall

If you installed intelmq with native packages: Use the package management tool to remove the package intelmq. These tools do not remove configuration by default.

If you installed manually via pip (note that this also deletes all configuration and possibly data):

pip3 uninstall intelmq
rm -r /opt/intelmq