This change split the inlet component into a simpler inlet and a new
outlet component. The new inlet component receive flows and put them in
Kafka, unparsed. The outlet component takes them from Kafka and resume
the processing from here (flow parsing, enrichment) and puts them in
ClickHouse.
The main goal is to ensure the inlet does a minimal work to not be late
when processing packets (and restart faster). It also brings some
simplification as the number of knobs to tune everything is reduced: for
inlet, we only need to tune the queue size for UDP, the number of
workers and a few Kafka parameters; for outlet, we need to tune a few
Kafka parameters, the number of workers and a few ClickHouse parameters.
The outlet component features a simple Kafka input component. The core
component becomes just a callback function. There is also a new
ClickHouse component to push data to ClickHouse using the low-level
ch-go library with batch inserts.
This processing has an impact on the internal representation of a
FlowMessage. Previously, it was tailored to dynamically build the
protobuf message to be put in Kafka. Now, it builds the batch request to
be sent to ClickHouse. This makes the FlowMessage structure hides the
content of the next batch request and therefore, it should be reused.
This also changes the way we decode flows as they don't output
FlowMessage anymore, they reuse one that is provided to each worker.
The ClickHouse tables are slightly updated. Instead of using Kafka
engine, the Null engine is used instead.
Fix#1122
We introduce SrcVlan and DstVlan for that. On next commit, a user will
be able to enable/disable columns. Adding columns will still need to
have code for that.
This is a huge change to make the various subcomponents of the inlet use
the schema to generate the protobuf. For it to make sense, we also
modify the way we parse flows to directly serialize non-essential fields
to Protobuf.
The performance is mostly on par with the previous commit. We are a bit
less efficient because we don't have a fixed structure, but we avoid
loosing too much performance by not relying on reflection and keeping
the production of messages as code. We use less of Goflow2: raw flow
parsing is still done by Goflow2, but we don't use the producer part
anymore. This helps a bit with the performance as we parse less.
Overall, we are 20% than the previous commit and twice faster than the
1.6.4!
```
goos: linux
goarch: amd64
pkg: akvorado/inlet/flow
cpu: AMD Ryzen 5 5600X 6-Core Processor
BenchmarkDecodeEncodeNetflow
BenchmarkDecodeEncodeNetflow/with_encoding
BenchmarkDecodeEncodeNetflow/with_encoding-12 151484 7789 ns/op 8272 B/op 143 allocs/op
BenchmarkDecodeEncodeNetflow/without_encoding
BenchmarkDecodeEncodeNetflow/without_encoding-12 162550 7133 ns/op 8272 B/op 143 allocs/op
BenchmarkDecodeEncodeSflow
BenchmarkDecodeEncodeSflow/with_encoding
BenchmarkDecodeEncodeSflow/with_encoding-12 94844 13193 ns/op 9816 B/op 295 allocs/op
BenchmarkDecodeEncodeSflow/without_encoding
BenchmarkDecodeEncodeSflow/without_encoding-12 92569 12456 ns/op 9816 B/op 295 allocs/op
```
There was a tentative to parse sFlow packets with gopackets, but the
adhoc parser used here is more performant.
This is a bit less type-safe. We could keep type safety by redefining
all the consts in `query_consts.go` in `common/schema`, but this is
pointless as the goal is to have arbitrary dimensions at some point.
We introduce an leaky abstraction for flows schema and use it for
migrations as a first step.
For views and dictionaries, we stop relying on a hash to know if they
need to be recreated, but we compare the select statements with our
target statement. This is a bit fragile, but strictly better than the
hash.
For data tables, we add the missing columns.
We give up on the abstraction of a migration step and just rely on
helper functions to get the same result. The migration code is now
shorter and we don't need to update it when adding new columns.
This is a preparatory work for #211 to allow a user to specify
additional fields to collect.
