OSMC 2021 | Handling 250K flows per second with OpenNMS: a case study

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What does it take to go from no flow support, to handling huge volumes of heterogeneous flow data in a 100% open-source monitoring stack, in a real-world environment? Expect a brief refresher on flows, an overview of the customer environment, and discussion of the engineering challenges faced. A medium dive follows into the movement of flow data from ingest to query and display, the solution architecture as it exists today, and lessons learned and their application to the project roadmap.

Handling 250K flows per second with OpenNMS
Jeff Gehlbach • Technical Product Manager • The OpenNMS Group, Inc. • jeffg@opennms.com
Open Source Monitoring Conference • Nürnberg 2021-11-09
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Open Source Monitoring Conference • Nürnberg
Agenda
2021-11-09
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1.Refresher on flows
2.Architectural overview
3.Nephron and streaming analytics
4.The future of flows in OpenNMS
5.Live Q&A

Open Source Monitoring Conference • Nürnberg
Anatomy of a flow
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Source: Ominike, Akpovi. (2016). Generating Netflow Traces for Network Configurations.

Open Source Monitoring Conference • Nürnberg
Flow protocols
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Source: Graham, Mark. (2017). An IPFIX Primer. 10.13140/RG.2.2.33426.35523.

Dissected example Important fields for our purposes
• Src addr
• Dst addr
• Src port
• Dst port
• Octets
• Duration
• **padding (ingress vs. egress)
NetFlow v5 export packet
Open Source Monitoring Conference • Nürnberg 2021-11-09
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Image: Jesse White

Open Source Monitoring Conference • Nürnberg
NetFlow v9 export packet
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Source: Cisco Systems

Open Source Monitoring Conference • Nürnberg
SNMP vs. sFlow vs. NetFlow
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Protocol SNMP sFlow NetFlow (v9) IPFIX
Type of information MIB counter Partial packets chosen
by sampling
Flow
Amount of data Small Large (depending on
sampling rate)
Between SNMP and sFlow (depending on sampling
rate and flow creation conditions)
Collectable
information
Amount of data across
interface
Data from data-link layer
(containing packet
header and data of
partial packet payload)
Data from data-link layer to transport layer
Data other than the
above is collected by
vendor extensions
Status of
standardization
RFC3411, RFC3418, etc.
(standard)
RFC3176 (informational
by InMon)
RFC3954 (informational
by Cisco)
Stage immediately
before publication as an
RFC (standard)
Source: Irino, Katayama, Chaki. (2007). Flow-based Network Measurement— NetFlow & IPFIX; NTT Technical Review

• A platform to collect, persist, and visualize flows, with support for:
• NetFlow v5
• NetFlow v9
• IPFIX
• sFlow
• Inventory enrichment (map flows to OpenNMS nodes)
• Application classification (port == 666 && ipaddr like 192.168.1-2.*
= quake3)
• Horizontal scale (battle-tested with 300K+ flows/sec)
• Enterprise reporting (push reports via PDF)
• Top K stats by interface, application, host, conversation, w/QOS
Open Source Monitoring Conference • Nürnberg
OpenNMS provides
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Open Source Monitoring Conference • Nürnberg
Flow visualization
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Open Source Monitoring Conference • Nürnberg
OpenNMS Horizon 29 flow pipeline (full scale, self-hosted)
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Exporter
Minion Kafka Sentinel Kafka ES
Nephron
Flink
PostgreSQL
OpenNMS
Core
Grafana
OpenMetrics
TSDB
(Cortex)
Parse,
Enrich,
Forward
Enrich,
Forward
Enrich,
Tag
Streaming
Analytics

Flows at scale
● 800 routers generating flows
● 1 interface on most routers
○ 2-4 interfaces on some
● 6 million+ flows per interface
per hour
Open Source Monitoring Conference • Nürnberg 2021-11-09
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Image: Jesse White

Open Source Monitoring Conference • Nürnberg
Challenges with just-in-time flow statistics
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• Can’t respond to queries fast enough
• Queries currently time out after 30+ minutes
• Customer requirements:
• Must be able to render dashboard in 10 seconds or less
• Must be able to render 30 day report in 10 minutes or less
• The stats:
• Top N over 4 billion documents
• 120000 unique hosts
• 6000 unique applications

Open Source Monitoring Conference • Nürnberg
Challenges with streaming flow analysis
2021-11-09
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• Time-domain problems
• Elements (flow data) must be grouped by time window
• Elements may arrive early, on time, or late
• Even with perfect clock sync, processing time may introduce time-lag
• Apache Beam tries to help with this
• It’s still a big engineering effort to get it right
• See http://streamingbook.net/figures (esp. figures 6-9 and 6-11)

Open Source Monitoring Conference • Nürnberg
The future of flows in OpenNMS
2021-11-09
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• Reduce complexity of the solution
• Eliminate the need for a Flink cluster to run Nephron (pipe dream?)
• Help improve Cortex support for high-cardinality data
• Eliminate need for Elasticsearch, ideally
• Build a Kubernetes operator (in progress)

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OSMC 2021 | Handling 250K flows per second with OpenNMS: a case study

1. Handling 250K flows per second with OpenNMS Jeff Gehlbach • Technical Product Manager • The OpenNMS Group, Inc. • jeffg@opennms.com Open Source Monitoring Conference • Nürnberg 2021-11-09 1

2. Open Source Monitoring Conference • Nürnberg Agenda 2021-11-09 2 1.Refresher on flows 2.Architectural overview 3.Nephron and streaming analytics 4.The future of flows in OpenNMS 5.Live Q&A

3. Open Source Monitoring Conference • Nürnberg Anatomy of a flow 2021-11-09 3 Source: Ominike, Akpovi. (2016). Generating Netflow Traces for Network Configurations.

4. Open Source Monitoring Conference • Nürnberg Flow protocols 2021-11-09 4 Source: Graham, Mark. (2017). An IPFIX Primer. 10.13140/RG.2.2.33426.35523.

5. Dissected example Important fields for our purposes • Src addr • Dst addr • Src port • Dst port • Octets • Duration • **padding (ingress vs. egress) NetFlow v5 export packet Open Source Monitoring Conference • Nürnberg 2021-11-09 5 Image: Jesse White

6. Open Source Monitoring Conference • Nürnberg NetFlow v9 export packet 2021-11-09 6 Source: Cisco Systems

7. Open Source Monitoring Conference • Nürnberg SNMP vs. sFlow vs. NetFlow 2021-11-09 7 Protocol SNMP sFlow NetFlow (v9) IPFIX Type of information MIB counter Partial packets chosen by sampling Flow Amount of data Small Large (depending on sampling rate) Between SNMP and sFlow (depending on sampling rate and flow creation conditions) Collectable information Amount of data across interface Data from data-link layer (containing packet header and data of partial packet payload) Data from data-link layer to transport layer Data other than the above is collected by vendor extensions Status of standardization RFC3411, RFC3418, etc. (standard) RFC3176 (informational by InMon) RFC3954 (informational by Cisco) Stage immediately before publication as an RFC (standard) Source: Irino, Katayama, Chaki. (2007). Flow-based Network Measurement— NetFlow & IPFIX; NTT Technical Review

8. • A platform to collect, persist, and visualize flows, with support for: • NetFlow v5 • NetFlow v9 • IPFIX • sFlow • Inventory enrichment (map flows to OpenNMS nodes) • Application classification (port == 666 && ipaddr like 192.168.1-2.* = quake3) • Horizontal scale (battle-tested with 300K+ flows/sec) • Enterprise reporting (push reports via PDF) • Top K stats by interface, application, host, conversation, w/QOS Open Source Monitoring Conference • Nürnberg OpenNMS provides 2021-11-09 8

9. Open Source Monitoring Conference • Nürnberg Flow visualization 2021-11-09 9

10. Open Source Monitoring Conference • Nürnberg OpenNMS Horizon 29 flow pipeline (full scale, self-hosted) 2021-11-09 10 Exporter Minion Kafka Sentinel Kafka ES Nephron Flink PostgreSQL OpenNMS Core Grafana OpenMetrics TSDB (Cortex) Parse, Enrich, Forward Enrich, Forward Enrich, Tag Streaming Analytics

11. Flows at scale ● 800 routers generating flows ● 1 interface on most routers ○ 2-4 interfaces on some ● 6 million+ flows per interface per hour Open Source Monitoring Conference • Nürnberg 2021-11-09 11 Image: Jesse White

12. Open Source Monitoring Conference • Nürnberg Challenges with just-in-time flow statistics 2021-11-09 12 • Can’t respond to queries fast enough • Queries currently time out after 30+ minutes • Customer requirements: • Must be able to render dashboard in 10 seconds or less • Must be able to render 30 day report in 10 minutes or less • The stats: • Top N over 4 billion documents • 120000 unique hosts • 6000 unique applications

13. Open Source Monitoring Conference • Nürnberg Challenges with streaming flow analysis 2021-11-09 13 • Time-domain problems • Elements (flow data) must be grouped by time window • Elements may arrive early, on time, or late • Even with perfect clock sync, processing time may introduce time-lag • Apache Beam tries to help with this • It’s still a big engineering effort to get it right • See http://streamingbook.net/figures (esp. figures 6-9 and 6-11)

14. Open Source Monitoring Conference • Nürnberg The future of flows in OpenNMS 2021-11-09 14 • Reduce complexity of the solution • Eliminate the need for a Flink cluster to run Nephron (pipe dream?) • Help improve Cortex support for high-cardinality data • Eliminate need for Elasticsearch, ideally • Build a Kubernetes operator (in progress)

15. Demo

16. Conclusion

OSMC 2021 | Handling 250K flows per second with OpenNMS: a case study

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