Tutorial: Add BGP Analytics to your Imply netflow analysis

Jun 08, 2020
Eric Graham

Imply is a real-time data platform for self-service analytics. It is very well suited for high performance analytics against event-driven data. One of the common use cases is to store, analyze, and visualize different types of networking data (NetFlow v5/v9, sFlow, IPFIX, etc.). I covered this in a previous blog post “Tutorial: An End-to-end Streaming Analytics Stack for Network Telemetry Data”.

For some network operators additional visibility is important. You might want to understand how you reach your customers through the Internet. Maybe you want to understand your top next-hop ASNs to determine your transit cost, or maybe performance has dropped and a specific ASN is the culprit, or you use BGP for internal routing with limited visibility into how traffic moves through your network. There are many use cases analyzing netflow with BGP.

In this tutorial, we will step through how to:

  • Set up Imply, Kafka, and pmacct
  • Add BGP visibility to your netflow
  • Load your BGP routing table to a Kafka topic
  • Ingest it in Imply

Want to know more about BGP and its use for Internet routing? Checkout the following URL:
https://searchnetworking.techtarget.com/feature/BGP-tutorial-The-routing-protocol-that-makes-the-Internet-work

Prerequisites

  • A bare metal server or cloud instance (such as an AWS m5d.xlarge) with 16GB RAM, 100GB of disk, and an ethernet interface.
  • A server running Linux.
  • Sudo or root access on the server.
  • A router, switch, firewall, or host that can send networking data.
  • A router that can peer BGP and has a full or partial routing table (not default route). I used exaBGP for my setup with super-smash-brogp to generate BGP updates with the right attributes. It worked great for testing. (Email me if you would like additional setup details for this eric.graham@imply.io.)

The architecture we will be using looks like the following:

Install supporting packages for pmacct

  • Install libpcap. This should be available in your Linux repository.

$ sudo apt-get install libpcap0.8

  • Install librdkafka and librdkafka-dev.

$ sudo apt-get install librdkafka-dev
$ sudo apt-get install librdkafka1

  • Set environment variables for libraries. $ export KAFKA_LIBS="-L/usr/lib/x86_64-linux-gnu -lrdkafka" $ export KAFKA_CFLAGS="-I/usr/include/librdkafka"
  • Download and extract the following jansson file:
    http://www.digip.org/jansson/releases/jansson-2.12.tar.gz
  • Set environment variable for libraries: $ export JANSSON_CFLAGS="-I/usr/local/include/" $ export JANSSON_LIBS="-L/usr/local/lib -ljansson"
  • While in the jansson directory created during extract, compile jansson by running: $ ./configure $ make $ make install

Install and compile pmacct

  • Use the most current version of pmacct
  • cd to the preferred installation directory
  • git clone https://github.com/pmacct/pmacct.git
  • cd to the pmacct directory
  • Run the following commands:
$ ./configure –enable-kafka –enable-jansson --enable-bgp-bins
$ sudo make
$ sudo make install
  • Create a bgp.map file for pmacct/nfacctd that includes the following. This maps the BGP router-id from your router to the netflow source IP. Highlighted selections need to be updated for your environment.

bgp_ip=2.2.2.2 ip=192.168.2.5

  • Create your nfacctd (a binary installed with pmacct) configuration file with the following information. Modify the highlighted areas to add your relevant information. Verify nfacctd is working before removing the # in front of daemonize so logs are displayed at the terminal. Once you know everything is working uncomment this and restart nfacctd.
!
#Kafka topic for netflow with BGP
kafka_topic: pmacct 
kafka_broker_host: 10.1.9.106
kafka_broker_port: 9092 
kafka_refresh_time: 1
kafka_refresh_time: 10 

plugins: kafka
#daemonize: true 
#interface you are listening on for netflow
pcap_interface: ens5 
#IP you are listening to for netflow
nfacctd_ip: 10.1.9.106 
#Netflow port you are sending to from your router
nfacctd_port: 20013 
#debug[default]: true
nfacctd_time_new: true

#Kafka topic to send your BGP routing table to
bgp_table_dump_kafka_topic: pmacct.bgp
bgp_table_dump_refresh_time: 60
bgp_daemon: true
#IP on your host you are listening on
bgp_daemon_ip: 10.1.9.106
bgp_daemon_max_peers: 100
nfacctd_net: bgp
nfacctd_as: bgp
#Local BGP ASN. This should be the same as your router ASN for iBGP
bgp_daemon_as: 65001

#Point to the location you created above for your bgp.map file
bgp_agent_map: /opt/pmacct-1.7.2/bgp.map

aggregate: src_host, dst_host,in_iface, out_iface, timestamp_start, timestamp_end, src_port, dst_port, proto, tos, tcpflags, tag, src_as, dst_as, peer_src_as, peer_dst_as, peer_src_ip, peer_dst_ip, local_pref, as_path

Router configuration

  • Configure your router to send flow data to nfacctd daemon based on the config above.
  • In your router neighbor configuration the router should be defined as a Route reflector and nfacctd a Route Reflector Client.

Install Imply

  • Download the most recent Imply distribution by going to the following URL:
    https://imply.io/get-started
  • Refer to the following quickstart for installation help and system requirements:
    https://docs.imply.io/on-prem/quickstart
  • Modify conf-quickstart/druid/_common/common.runtime.properties with the right directories for segments and logs. If you have plenty of local disk you can keep the default configuration. A good reference is the Imply quickstart documentation:
    https://docs.imply.io/on-prem/quickstart
  • Start Imply from the Imply directory with the quickstart configuration by typing the following:

$ sudo bin/supervise -c conf/supervise/quickstart.conf &

Install Kafka

Note: The Imply distribution already includes Apache Zookeeper, which Kafka will use when you start it.

  • Start Kafka with the following command from within the Kafka directory:

$ sudo ./bin/kafka-server-start.sh config/server.properties &

  • Create two Kafka topics (one for netflow with BGP and one for your BGP routing table) using the following command where < topic name > is replaced with the name you want – such as netflow and pmacct.bgp From the Kafka installation directory, run:
$ sudo ./bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic netflow
sudo ./bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic pmacct.bgp

Start nfacctd

  • Nfacctd (included with pmacct) can be started with the configuration above. This will start a process that listens for incoming network flows. Once you have added the nfacctd daemon as a neighbor in your router (next bullet down) you should also see BGP state move to OPEN state with your router.

$ sudo nfacctd -f ./nfacct.conf

  • On your router configure a neighbor to match what you added in your nfacct.conf file. You should have the same ASN for your router and the nfacct.conf to create an iBGP peer.
  • Start sending network flows to the system you have just set up. Make sure to change your security rules to allow the source IP of the network flow sender and the destination port that you configured on the router. If everything is working properly you should see nfacctd display the number of received packets. Note that this does not happen all the time — counters are incremented only when a flow export takes place from the router and this process is based on flow export timers. When you see packets registered, and the BGP peer is up, you can check your Kafka consumer by running the following from the Kafka installation directory.:
$ bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic *<topic name>* --from-beginning

 $ bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic pmacct.bgp --from-beginning

Connect Kafka and Imply

  • Open the Imply UI by opening a browser and either going to localhost:9095 (if browser is being run from your localhost) or . Remember to modify your security rules to allow destination port 9095 from your source IP.
  • Select the Data/+Load Data (left tab).
  • In the Druid data loader select “Load data”, “Create a new spec”, Apache Kafka”. “Connect data on right”
  • Fill in the details for the Kafka process including IP:consumer port (typically 9092) and the topic name that you created previously (e.g. 192.168.1.2:9092). You will be creating two data sources. One for netflow with BGP and the other specifically for the BGP routing table. Therefore this process will need to be executed twice.
  • Select “Sample and continue”
  • Select “Next” for the remaining screens to start loading your network flows into Imply.
  • Start visualizing BGP with netflow. In the following dashboard I can view top BGP AS_Paths with destination IPs or top outgoing interfaces and the destination ASNs. I could further enhance this visualization by mapping ASN to AS-Name using JOINs or lookups, which I have done below (lower right).

From my routing table I can use my dimensions to visualize data, for example, which AS_Paths do I use to reach my top prefixes.

In this blog we showed you how to combine the power of netflow with BGP routing to further enhance your network visibility. Whether you are a CDN, Service provider, mobile operator or just a large enterprise, BGP visibility matters. In the future please stay tuned for additional blogs providing a deeper dive into BGP analytics use cases and alerting.

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Upserts and Data Deduplication with Druid

A look at what can be done with Druid for upserts and data deduplication.

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Jul 01, 2022

What Developers Can Build with Apache Druid

We obviously talk a lot about #ApacheDruid on here. But what are folks actually building with Druid? What is a modern analytics application, exactly? Let's find out

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Jun 29, 2022

When Streaming Analytics… Isn’t

Nearly all databases are designed for batch processing, which leaves three options for stream analytics.

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Jun 29, 2022

Apache Druid vs. Snowflake

Elasticity is important, but beware the database that can only save you money when your application is not in use. The best solution will have excellent price-performance under all conditions.

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Jun 22, 2022

Druid 0.23 – Features And Capabilities For Advanced Scenarios

Many of Druid’s improvements focus on building a solid foundation, including making the system more stable, easier to use, faster to scale, and better integrated with the rest of the data ecosystem. But for...

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Jun 22, 2022

Introducing Apache Druid 0.23

Apache Druid 0.23.0 contains over 450 updates, including new features, major performance enhancements, bug fixes, and major documentation improvements.

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Jun 20, 2022

An Opinionated Guide to Component APIs

We have collected a number of guidelines for React component APIs that make components more predictable in terms of behavior and performance.

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Jun 10, 2022

Druid Architecture & Concepts

In a world full of databases, learn how Apache Druid makes real-time analytics apps a reality in this Whitepaper from Imply

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May 25, 2022

3 decisions that shaped the Polaris UI

Imply Polaris is a fully managed database-as-a-service for building realtime analytics applications. John is the tech lead for the Polaris UI, known internally as the Unified App. It began with a profound question:...

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May 19, 2022

How Imply Polaris takes a security-first approach

A primer for developers on security tools and controls available in Imply Polaris

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May 17, 2022

Imply Raises $100MM in Series D funding

There is a new category within data analytics emerging which is not centered in the world of reports and dashboards (the purview of data analysts and data scientists), but instead centered in the world of applications...

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May 11, 2022

Imply Named “Cool Database Vendor” by CRN

There can’t be one database good at everything. When it comes to real-time analytics, you need a database built for it.

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May 11, 2022

Living the Stream

We are in the early stages of a stream revolution, as developers build modern transactional and analytic applications that use real-time data continuously delivered.

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May 02, 2022

Migrating Data from ClickHouse to Imply Polaris

In this blog, we’ll review the simple steps to export data from ClickHouse in a format that is easy to ingest into Polaris.

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