Your memory issues stem from multiple architectural problems. Let me address each area systematically for cloud-native ETL performance.
Memory Profiling: First, get detailed visibility into memory allocation. Add these JVM flags to your Kubernetes deployment:
env:
- name: JAVA_OPTS
value: |
-Xms4g -Xmx6g
-XX:+UseG1GC
-XX:+HeapDumpOnOutOfMemoryError
-XX:HeapDumpPath=/var/log/heapdump.hprof
-Xlog:gc*:file=/var/log/gc.log
When OOM occurs, you’ll get a heap dump for analysis with Eclipse MAT or VisualVM. This reveals exactly which objects consume memory. In your case, I suspect you’ll find string objects and intermediate result sets dominating the heap.
Streaming Architecture: Cognos data preparation must process data in chunks, not load entire files. Redesign your ETL flow:
// Bad: Loads entire file
List<Record> allRecords = readFile("data.csv");
for (Record r : allRecords) { transform(r); }
// Good: Streams with fixed buffer
BufferedReader reader = new BufferedReader(new FileReader("data.csv"), 8192);
String line;
while ((line = reader.readLine()) != null) {
processLine(line);
// Buffer flushed every 1000 lines
}
Implement a streaming pipeline with backpressure - if downstream processing slows down, pause reading from source. This prevents memory accumulation.
Kubernetes Resource Limits: Set appropriate resource configuration:
resources:
requests:
memory: "6Gi"
cpu: "2000m"
limits:
memory: "8Gi"
cpu: "4000m"
Requests determine scheduling, limits prevent runaway consumption. The 25% buffer (6Gi request vs 8Gi limit) allows for temporary spikes without OOMKilled errors. Also implement liveness and readiness probes that check memory usage - if a pod exceeds 90% of limit, mark it unhealthy so Kubernetes routes traffic elsewhere.
Garbage Collection Tuning: G1GC is optimal for containerized ETL workloads. Configure it properly:
-XX:+UseG1GC
-XX:MaxGCPauseMillis=200
-XX:G1HeapRegionSize=16m
-XX:InitiatingHeapOccupancyPercent=45
-XX:G1ReservePercent=10
G1HeapRegionSize=16m works well for large datasets. InitiatingHeapOccupancyPercent=45 triggers concurrent GC earlier, preventing full GC pauses. Monitor GC overhead - if it exceeds 10% of CPU time, you need more memory or better streaming.
For your specific string allocation problem, use StringBuilder with capacity:
StringBuilder sb = new StringBuilder(256);
for (String field : fields) {
sb.append(field).append(",");
}
String result = sb.toString();
sb.setLength(0); // Reuse for next row
Also implement object pooling for frequently allocated objects. Use Apache Commons Pool to reuse transformation objects instead of creating new ones per row.
Finally, consider splitting large files before processing. Use a file splitter that creates 500MB chunks, then process each chunk in a separate pod. This horizontal scaling approach is more cloud-native than trying to process 5GB files in single containers. Implement this with Kubernetes Jobs that spawn multiple pods in parallel, each processing a file chunk.