This repository contains a set of tools and examples for GoFlow, a NetFlow/IPFIX/sFlow collector by Cloudflare.
The demo directory contains a startup file for an example pipeline including:
- GoFlow: an sFlow collector
- A mock collector
- Kafka/Zookeeper
- A database (Postgres/clickhouse)
- An inserter: to insert the flows in a database (for Postgres)
It will listen on port 6343/UDP for sFlow and 2055/UDP for NetFlow.
The protobuf provided in this repository is a light version of the GoFlow original one. Only a handful of fields will be inserted.
A basic pipeline looks like this:
+------+ +-----+
sFlow/NetFlow |goflow+--------->Kafka|
+------+ +-----+
|
+--------------+
Topic: flows | |
| |
+-----v----+ +-v---------+
| inserter | |new service|
+----------+ +-----------+
|
|
+--v--+
| DB |
+-----+
You can add a processor that would enrich the data by consuming from Kafka and re-injecting the data into Kafka or directly into the database.
For instance, IP addresses can be mapped to countries, ASN or customer information.
A suggestion is extending the GoFlow protobuf with new fields.
A mock insertion replaces the GoFlow decoding part. A mocker generates protobuf messages and sends them to Kafka.
Clone the repository, then run the following (for Postgres):
$ cd compose
$ docker-compose -f docker-compose-postgres-mock.yaml
Wait a minute for all the components to start.
You can connect on the local Grafana http://localhost:3000 (admin/admin) to look at the flows being collected.
If you want to send sFlow/NetFlow/IPFIX to a GoFlow, run the following:
Using Postgres:
$ cd compose
$ docker-compose -f docker-compose-postgres-collect.yml
Using Clickhouse (see next section):
$ cd compose
$ docker-compose -f docker-compose-clickhouse-collect.yml
Keep in mind this is a development/prototype setup. Some components will likely not be able to process more than a few thousands rows per second. You will likely have to tweak configuration statements, number of workers.
Using a production setup, GoFlow was able to process more than +100k flows per seconds and insert them in a Clickhouse database.
If you choose to visualize in Grafana, you will need a Clickhouse Data source plugin. You can connect to the compose Grafana which has the plugin installed.
The insertion is handled natively by Clickhouse:
- Creates a table with a Kafka Engine.
- Uses Protobuf format.
Note: the protobuf messages to be written with their lengths.
Clickhouse will connect to Kafka periodically and fetch the content. Materialized views allow to store the data persistently and aggregate over fields.
To connect to the database, you have to run the following:
$ docker exec -ti compose_db_1 clickhouse-client
Once in the client CLI, a handful of tables are available:
flowsis directly connected to Kafka, it fetches from the current offsetflows_rawcontains the materialized view offlowsflows_5mcontains 5-minutes aggregates of ASN
Commands example:
:) DESCRIBE flows_raw
DESCRIBE TABLE flows_raw
┌─name───────────┬─type────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐
│ Date │ Date │ │ │ │ │ │
│ TimeReceived │ DateTime │ │ │ │ │ │
│ TimeFlowStart │ DateTime │ │ │ │ │ │
│ SequenceNum │ UInt32 │ │ │ │ │ │
│ SamplingRate │ UInt64 │ │ │ │ │ │
│ SamplerAddress │ FixedString(16) │ │ │ │ │ │
│ SrcAddr │ FixedString(16) │ │ │ │ │ │
│ DstAddr │ FixedString(16) │ │ │ │ │ │
│ SrcAS │ UInt32 │ │ │ │ │ │
│ DstAS │ UInt32 │ │ │ │ │ │
│ EType │ UInt32 │ │ │ │ │ │
│ Proto │ UInt32 │ │ │ │ │ │
│ SrcPort │ UInt32 │ │ │ │ │ │
│ DstPort │ UInt32 │ │ │ │ │ │
│ Bytes │ UInt64 │ │ │ │ │ │
│ Packets │ UInt64 │ │ │ │ │ │
└────────────────┴─────────────────┴──────────────┴────────────────────┴─────────┴──────────────────┴────────────────┘
:) SELECT Date,TimeReceived,IPv6NumToString(SrcAddr), IPv6NumToString(DstAddr), Bytes, Packets FROM flows_raw;
SELECT
Date,
TimeReceived,
IPv6NumToString(SrcAddr),
IPv6NumToString(DstAddr),
Bytes,
Packets
FROM flows_raw
┌───────Date─┬────────TimeReceived─┬─IPv6NumToString(SrcAddr)─┬─IPv6NumToString(DstAddr)─┬─Bytes─┬─Packets─┐
│ 2020-03-22 │ 2020-03-22 21:26:38 │ 2001:db8:0:1::80 │ 2001:db8:0:1::20 │ 105 │ 63 │
│ 2020-03-22 │ 2020-03-22 21:26:38 │ 2001:db8:0:1::c2 │ 2001:db8:0:1:: │ 386 │ 43 │
│ 2020-03-22 │ 2020-03-22 21:26:38 │ 2001:db8:0:1::6b │ 2001:db8:0:1::9c │ 697 │ 29 │
│ 2020-03-22 │ 2020-03-22 21:26:38 │ 2001:db8:0:1::81 │ 2001:db8:0:1:: │ 1371 │ 54 │
│ 2020-03-22 │ 2020-03-22 21:26:39 │ 2001:db8:0:1::87 │ 2001:db8:0:1::32 │ 123 │ 23 │
To look at aggregates (optimizing will run the summing operation). The Nested structure allows to have sum per structures (in our case, per Ethernet-Type).
:) OPTIMIZE TABLE flows_5m;
OPTIMIZE TABLE flows_5m
Ok.
:) SELECT * FROM flows_5m WHERE SrcAS = 65001;
SELECT *
FROM flows_5m
WHERE SrcAS = 65001
┌───────Date─┬────────────Timeslot─┬─SrcAS─┬─DstAS─┬─ETypeMap.EType─┬─ETypeMap.Bytes─┬─ETypeMap.Packets─┬─ETypeMap.Count─┬─Bytes─┬─Packets─┬─Count─┐
│ 2020-03-22 │ 2020-03-22 21:25:00 │ 65001 │ 65000 │ [34525] │ [2930] │ [152] │ [4] │ 2930 │ 152 │ 4 │
│ 2020-03-22 │ 2020-03-22 21:25:00 │ 65001 │ 65001 │ [34525] │ [1935] │ [190] │ [3] │ 1935 │ 190 │ 3 │
│ 2020-03-22 │ 2020-03-22 21:25:00 │ 65001 │ 65002 │ [34525] │ [4820] │ [288] │ [6] │ 4820 │ 288 │ 6 │
Regarding the storage of IP addresses:
At the moment, the current Clickhouse table does not perform any transformation of the addresses before insertion.
The bytes are inserted in a FixedString(16) regardless of the family (IPv4, IPv6).
In the dashboards, the function IPv6NumToString(SrcAddr) is used.
For example, 192.168.1.1 will end up being 101:a8c0::
WITH toFixedString(reinterpretAsString(ipv4), 16) AS ipv4c
SELECT
'192.168.1.1' AS ip,
IPv4StringToNum(ip) AS ipv4,
IPv6NumToString(ipv4c) AS ipv6
┌─ip──────────┬───────ipv4─┬─ipv6───────┐
│ 192.168.1.1 │ 3232235777 │ 101:a8c0:: │
└─────────────┴────────────┴────────────┘In order to convert it:
WITH IPv6StringToNum(ip) AS ipv6
SELECT
'101:a8c0::' AS ip,
reinterpretAsUInt32(ipv6) AS ipv6c,
IPv4NumToString(ipv6c) AS ipv4
┌─ip─────────┬──────ipv6c─┬─ipv4────────┐
│ 101:a8c0:: │ 3232235777 │ 192.168.1.1 │
└────────────┴────────────┴─────────────┘Which for instance to display either IPv4 or IPv6 in a single query:
SELECT
if(EType = 0x800, IPv4NumToString(reinterpretAsUInt32(SrcAddr)), IPv6NumToString(SrcAddr) AS SrcIPThis will be fixed in future dashboard/db schema version.
This repository is an example and does not offer any warranties. I try to update it whenever I can. Contributions are welcome.
The main purpose is for users to get started quickly and provide a basic system. This should not be used in production.
I received requests to publish the Flink aggregator source code as you may have seen it being used in GoFlow presentations. Unfortunately, we moved entirely towards Clickhouse, the old code has not been updated in a while. It may get published at some point but this is currently low priority.
The compose files don't bind to specific versions of the containers. You will likely need to down in order to clean the setup (volumes, network), push to resynchronize repositories like GoFlow and build to rebuild components like inserter .
$ docker-compose -f some-yaml-listed-above.yml down
$ docker-compose -f some-yaml-listed-above.yml pull
$ docker-compose -f some-yaml-listed-above.yml build
$ docker-compose -f some-yaml-listed-above.yml up
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