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Introduction
Network telemetry at Terabit scale traditionally forces a brutal trade‑off: ingest every flow and risk packet drops, or sample traffic and lose attack visibility. FastNetMon Advanced 2.0.379 shatters this dilemma with fully asynchronous Clickhouse support, sustaining 100,000 flow writes per second on a residential Terabit network without a single drop – a milestone that redefines real‑time DDoS protection and high‑velocity traffic analysis.
Learning Objectives
- Implement asynchronous database ingestion to eliminate write‑induced packet loss in high‑throughput environments
- Configure FastNetMon with Clickhouse for zero‑drop flow capture at 100K+ writes/sec
- Optimize Linux kernel, DPDK, and Clickhouse schema for Terabit‑scale residential network monitoring
You Should Know
1. Asynchronous Clickhouse Integration – The Game Changer
Traditional synchronous writes force packet processing to wait for database acknowledgement, creating a bottleneck. FastNetMon’s new asynchronous mode buffers flows in memory and writes to Clickhouse in non‑blocking batches, preventing backpressure.
Step‑by‑step configuration (Ubuntu 22.04):
Install FastNetMon Advanced (requires license) wget https://repo.fastnetmon.com/ubuntu/fastnetmon_advanced_2.0.379_amd64.deb sudo dpkg -i fastnetmon_advanced_2.0.379_amd64.deb Install Clickhouse sudo apt-get install apt-transport-https ca-certificates dirmngr sudo apt-key adv --keyserver keyserver.ubuntu.com --recv E0C56BD4 echo "deb https://repo.clickhouse.com/deb/stable/ main/" | sudo tee /etc/apt/sources.list.d/clickhouse.list sudo apt-get update && sudo apt-get install clickhouse-server clickhouse-client Configure FastNetMon for async Clickhouse sudo nano /etc/fastnetmon_advanced/fastnetmon.conf
Add these lines:
clickhouse_enable = yes clickhouse_async_mode = yes clickhouse_async_batch_size = 5000 clickhouse_async_flush_interval = 100 milliseconds clickhouse_host = 127.0.0.1 clickhouse_port = 9000 clickhouse_database = fastnetmon clickhouse_table = flows
Restart services:
sudo systemctl restart clickhouse-server sudo systemctl restart fastnetmon_advanced
2. Benchmarking Your Network for 100K Flows/Sec
Before trusting your setup, validate that your hardware and driver stack can sustain the claimed write rate.
Generate synthetic flow‑like traffic:
Install pktgen (Linux kernel module) sudo modprobe pktgen Use pktgen_sample03_burst_single_flow.sh from kernel samples Or use iperf3 with multiple parallel streams iperf3 -c 192.168.1.1 -P 50 -t 60 -l 64 small packets mimic flow samples
Monitor drops in real time:
Check kernel packet drops on interface (e.g., eth0) watch -n 1 'ethtool -S eth0 | grep -E "rx_dropped|rx_no_buffer"' Netstat extended counters netstat -s --statistics --raw | grep -i drop FastNetMon internal metrics tail -f /var/log/fastnetmon_advanced/fastnetmon.log | grep "flows_per_second"
3. Linux Kernel Tuning for Terabit‑Scale Packet Processing
Residential Terabit networks demand DPDK or AF_XDP to bypass kernel overhead. FastNetMon Advanced supports both.
DPDK setup (for Intel NICs):
Install DPDK sudo apt-get install dpdk dpdk-dev Bind interface to uio_pci_generic sudo modprobe uio_pci_generic sudo dpdk-devbind.py -b uio_pci_generic 0000:03:00.0 replace with your PCIe address Configure hugepages echo 4096 | sudo tee /proc/sys/vm/nr_hugepages
Kernel tuning if using traditional sockets (for smaller scales):
Increase socket buffers sudo sysctl -w net.core.rmem_max=134217728 sudo sysctl -w net.core.rmem_default=67108864 sudo sysctl -w net.ipv4.tcp_rmem="4096 87380 134217728" Multi‑queue RSS sudo ethtool -L eth0 combined 16 depending on CPU cores sudo sysctl -w net.core.rps_sock_flow_entries=32768
4. FastNetMon Advanced Configuration for Residential Networks
Residential networks have asymmetric traffic and many ephemeral flows. Optimize sampling and aggregation.
Configuration snippet (`/etc/fastnetmon_advanced/fastnetmon.conf`):
Capture both directions capture_direction = both Sample 1:1 (no sampling for zero‑drop requirement) sampling_rate = 1 Flow aggregation window (ms) aggregation_window = 100 Ignore local broadcast noise ignore_broadcast = yes Memory buffer before async write buffer_size_mb = 2048 DDoS thresholds (example for residential) ban_for_bandwidth = yes bandwidth_threshold = 1000000000 1 Gbps per IP ban_for_pps = yes pps_threshold = 50000
Enable sFlow/NetFlow input if needed:
echo "sflow_collector_enable = yes" >> /etc/fastnetmon_advanced/fastnetmon.conf echo "sflow_collector_port = 6343" >> /etc/fastnetmon_advanced/fastnetmon.conf
5. Clickhouse Schema Optimization for Time‑Series Flow Data
A poorly indexed Clickhouse table will cause writes to queue and eventually drop. Use the schema recommended for high‑velocity flow data.
Create optimized table:
CREATE DATABASE fastnetmon; CREATE TABLE fastnetmon.flows ( timestamp UInt32, src_ip IPv4, dst_ip IPv4, src_port UInt16, dst_port UInt16, protocol UInt8, packets UInt64, bytes UInt64, vlan_id UInt16 DEFAULT 0 ) ENGINE = MergeTree() ORDER BY (timestamp, src_ip, dst_ip) PARTITION BY toYYYYMMDD(toDateTime(timestamp)) SETTINGS index_granularity = 8192;
Enable deduplication for at‑most‑once semantics:
ALTER TABLE fastnetmon.flows MODIFY SETTING replicated_deduplication_window = 100, non_replicated_deduplication_window = 100;
Monitor write performance inside Clickhouse:
SELECT event_time, written_rows, written_bytes, query_duration_ms FROM system.query_log WHERE type = 'QueryFinish' AND query LIKE 'INSERT INTO fastnetmon.flows%' ORDER BY event_time DESC LIMIT 20;
6. Monitoring and Alerting on Drop Events
Even with asynchronous writes, drops can occur upstream. Build a detection stack.
Prometheus exporter for FastNetMon:
Install fastnetmon_exporter (community) wget https://github.com/example/fastnetmon_exporter/releases/download/v1.0/fastnetmon_exporter chmod +x fastnetmon_exporter ./fastnetmon_exporter --web.listen-address=":9102"
Alert rule (Prometheus):
groups:
- name: fastnetmon_alerts
rules:
- alert: PacketDropsDetected
expr: rate(fastnetmon_dropped_packets_total[bash]) > 0
annotations:
summary: "Packet drops at {{ $labels.instance }}"
Manual drop detection using `dropwatch`:
sudo apt-get install dropwatch sudo dropwatch -l kasan kernel drops
Run live to see if Clickhouse async batches ever overflow.
7. Troubleshooting High Write Latency in Clickhouse
Asynchronous mode buffers writes, but if Clickhouse cannot keep up, the buffer will fill and drops occur.
Check Clickhouse query execution time:
SELECT query, query_duration_ms FROM system.query_log WHERE type = 'QueryFinish' AND query LIKE 'INSERT%' ORDER BY query_duration_ms DESC LIMIT 10;
Increase background thread pools (`/etc/clickhouse-server/config.xml`):
<background_pool_size>32</background_pool_size> <background_schedule_pool_size>16</background_schedule_pool_size> <background_merges_mutations_concurrency_ratio>2</background_merges_mutations_concurrency_ratio>
Use `EXPLAIN` on a typical SELECT to detect slow reads (writes can be blocked by heavy reads):
EXPLAIN indexes = 1 SELECT count() FROM fastnetmon.flows WHERE timestamp > toUnixTimestamp(now() - 300);
Disk I/O tuning – move Clickhouse data to NVMe:
sudo systemctl stop clickhouse-server sudo mkdir -p /mnt/nvme/clickhouse sudo chown clickhouse:clickhouse /mnt/nvme/clickhouse Edit /etc/clickhouse-server/config.xml, set <path>/mnt/nvme/clickhouse/</path> sudo systemctl start clickhouse-server
What Undercode Say
- Asynchronous I/O is mandatory for true zero‑drop telemetry – FastNetMon proves that memory batching eliminates the storage bottleneck, allowing 100K writes/sec without losing a single packet.
- Clickhouse is no longer just an analytics database; it’s a real‑time streaming engine – With proper tuning (MergeTree, partitioning, background threads), it can ingest network flows at wire speed while remaining queryable.
- Residential Terabit networks require DPDK and kernel bypass – The software stack matters as much as hardware; the announcement implicitly validates that user‑space packet processing is now production‑grade for ISPs.
Prediction
Within 18 months, every major DDoS mitigation vendor will adopt fully asynchronous database backends – either Clickhouse or custom time‑series engines – as the baseline for any 100Gbps+ appliance. We will see the death of sampled flow export (NetFlow sampling) in favour of 1:1 capture at Terabit scale, driven by the falling cost of NVMe and the maturity of asynchronous frameworks. This shift will enable AI‑driven attack detection models that train on complete traffic histories, not probabilistic sketches, making DDoS mitigation both faster and more accurate. Expect open‑source forks of FastNetMon’s async logic to appear, democratizing zero‑drop monitoring for mid‑sized enterprises.
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Reported By: Podintsov We – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
