Most of the "fast data pipeline" folklore in the JVM world ends at the same place: go reactive, or go home. Netty, event loops, backpressure operators, the works. I wanted to find out whether Java 25's virtual threads let you write the boring, blocking, one-thread-per-connection version — and still move hundreds of thousands of messages a second. So I built jBMP, a collector for the BGP Monitoring Protocol, and pushed it until the database begged for mercy.
This is the story of what I built, and — more usefully — the three times I was wrong about where the time was going.
What's a BMP collector, and why care about throughput
BMP (RFC 7854) is how a router streams its BGP state to a monitoring station: it opens a TCP session and pushes a firehose of route monitoring messages (every prefix it learns), peer up/down events, and periodic statistics. A single big edge router or route reflector can dump millions of prefixes when a session comes up. A collector that watches a few hundred routers has to absorb that initial-dump thundering herd without falling over.
So the shape of the problem is: many long-lived TCP connections, each occasionally bursting huge volumes of structured binary messages that must be parsed and durably stored. Classic high-fan-in ingest.
jBMP splits into three services around a pure-Java protocol library:
- a collector that terminates BMP/TCP, parses the BMP envelope and the carried BGP-4 messages, and produces them to Kafka;
- a consumer that drains Kafka and bulk-loads PostgreSQL/TimescaleDB;
- a mock router to generate load.
Decision 1: one virtual thread per router
The collector's core is deliberately dumb:
// one of these runs per connected router, on its own virtual thread
while ((message = readNextBmpMessage(in)) != null) {
var parsed = parser.parse(message);
var enriched = enricher.enrich(parsed, context);
publisher.publish(enriched); // to Kafka
}
Blocking reads. Blocking parse. No callbacks, no state machine, no reassembly buffer that I have to thread through an event loop. Each router gets Thread.ofVirtual().start(...), and the JVM multiplexes thousands of these onto a handful of carrier threads. When a read blocks on the socket, the virtual thread is parked and its carrier is freed — exactly what an event loop does for you, except I never had to write the event loop.
This matters beyond aesthetics. BMP framing is stateful (a 6-byte common header gives you the length, then you read the rest). In a reactive pipeline that becomes a tedious incremental decoder. With a virtual thread it's a plain readFully. The code reads like the spec.
The lesson that surprised me: at no point in the entire performance investigation below did the threading model show up as a bottleneck. Virtual threads did their job and got out of the way.
Decision 2: a custom binary wire format, not Protobuf
The collector and consumer talk over Kafka. The obvious move is Protobuf or Avro. I didn't.
A parsed route-monitoring message is already a tight binary structure — prefixes are bytes, next-hops are bytes, AS-paths are arrays of integers. Re-encoding that into Protobuf means a schema round-trip, descriptor lookups, and a second allocation of everything. So jBMP ships a hand-rolled, length-prefixed binary codec: a one-byte presence bitmask for the optional extended families, then just the fields that are present. No reflection, no schema registry, a single byte[] per message.
Is this the right call for every project? No — you lose schema evolution tooling, and you own the forward-compatibility tests. But for a closed producer/consumer pair on the hot path, shaving the serialization layer to the bone is free throughput. (jBMP versions the format and keeps the decoder backward-compatible; that's the tax you pay for rolling your own.)
Decision 3: bulk binary COPY into the database
The consumer's job is to get rows into PostgreSQL/TimescaleDB as fast as the disk allows. Per-row INSERTs are a non-starter at these rates. jBMP renders each Kafka poll-batch directly into PostgreSQL's binary COPY stream and streams it in one shot: timestamptz as microseconds, cidr/inet in their native family-tagged form, AS-paths and communities as int[]/text[], the structured families as jsonb. The server ingests each value in its on-disk representation, skipping the text-parse-and-validate it does for a normal INSERT.
Alongside the append-only history there's a current-state projection (rib_state): one row per (peer, prefix), kept up to date with an idempotent INSERT … ON CONFLICT DO UPDATE / DELETE. That projection is rebuildable from the history, so it runs on a single background worker off the commit path — the consumer commits its Kafka offsets the moment the history is durable and lets the projection catch up behind it. Remember this detail; it comes back.
Now the part where I was wrong three times
I built a benchmark — a mock pushing 50 routers × 10 peers × thousands of prefixes — and started measuring the consumer's drain rate into the database. Here's where intuition failed.
Wrong #1: "it's the CPU / the decode"
A Java Flight Recorder profile said otherwise. Out of an entire drain, there were only ~300 CPU execution samples — the consumer was barely running Java code. It was blocked. Aggregating the jdk.SocketRead events by remote port showed ~57 seconds of aggregate wait reading responses from the database, and essentially nothing waiting on Kafka fetches. The bottleneck was I/O wait on the DB round trips, not the parser, not the codec. First lesson: profile before you optimise; the wide allocation-heavy decode I was sure would dominate was a rounding error.
Wrong #2: "more parallelism will fix it"
When I scaled the mock up, throughput collapsed — big bursts then multi-second stalls. I assumed a checkpoint storm and started tuning WAL and synchronous_commit. It changed nothing.
So I did the thing I should have done first: I took a thread dump during a stall and looked at pg_stat_activity at the same instant. The database connections were almost all idle, waiting on ClientRead — i.e. waiting for my client to send something. The bottleneck wasn't the DB at all in that moment. And the thread dump showed three consumer threads stuck deep inside writeStats() → commit(), blocked on a socket read for the commit response.
There it was. The low-volume statistics/peer writers were running inline on the same threads that do the route-monitor bulk COPY. The mock generated a burst of stats; each was its own little transaction; and the COPY threads that owned those partitions were head-of-line blocked behind thousands of tiny per-message round trips.
The fix in Spring Kafka was to consume the low-volume topics on a separate listener container with its own small thread pool, so a stats burst can never stall the bulk path. On the realistic load, sustained drain went from ~17k rows/s to ~110k. Second lesson: a thread dump taken during the stall is worth a hundred guesses, and "add threads" is not a strategy — where the work runs matters more than how much of it there is.
Wrong #3: "look how much faster mine is"
For a while my numbers looked spectacular — multiples faster than a comparable implementation on the same hardware. Then I checked the database and found the comparison was a lie I'd told myself.
The Kafka partition key is the router identity. I had — "cleverly" — derived that identity from the router's advertised system name, so my mock's 50 simulated routers fanned out across ~35 partitions and 12 consumer threads. The implementation I was comparing against derived the identity from the source IP; my mock's routers all shared one IP, so it collapsed to one partition and one consumer. I wasn't measuring better code. I was measuring 12× the parallelism.
When I aligned the identity derivation and re-ran at honest parity — same partitions, same full schema — the gap evaporated: ~19k vs ~19k rows/s, within run-to-run noise. Third lesson, and the one I'd tattoo on a benchmark: most benchmark "wins" are configuration artifacts. If your number is surprisingly good, the first hypothesis should be that the test is unfair, not that you're a genius.
Where it landed
jBMP sustains tens of thousands of rows/second per partition into a network-attached TimescaleDB and scales near-linearly as traffic spreads across routers/partitions — hundreds of thousands of rows/second in bursts across a few dozen partitions. The real ceiling, at parity, is the database's write path, not the JVM.
The three lessons generalise well beyond BMP:
- Profile before optimising — your intuition about the hot path is probably wrong.
- During a stall, dump the threads and the DB sessions — they'll tell you who's actually waiting on whom.
- Distrust a benchmark that flatters you — equalise the configuration before you believe the number.
The code is open source (Apache-2.0): https://github.com/lorenzopompili/jbmp
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