Performance Benchmarks

Proof of the pudding for scalability of Obsrv

Note: This is a work in progress page. Following results are from initial benchmarks. Detailed benchmarks will be added shortly once the benchmark exercise is completed

Cluster Size

Config Name	Config Value
Number of Nodes	4
Node Size	4 core, 16 Gb
PV size	1 TB
Installation Mode	Obsrv with monitoring and real-time storage

Processing Benchmarks

Processing benchmark is independent on number of datasets created, hence the strategy is to test with volume with all configurations enabled. Disabling any configuration is going to improve throughput

Configuration 1

1. Dedup turned on

2. De-normalization configured on 2 master datasets

3. Transformations configured on 2 fields

4. Event size of 1 kb

Results

Flink Configuration	Events per Min	Events per hour	Events per Day
1 CPU, 1GB, 1 task slot, 1 parallelism	~ 13k | 13 Mb	~ 750k | 780Mb	~ 18Million | 18Gb
2 CPU, 2GB, 2 task slot, 2 parallelism	~ 30k | 30 Mb	~ 1.8Million | 1.8Gb	~ 40Million | 40Gb
4 CPU, 4Gb, 4 task slots, 4 parallelism	In Progress	In Progress	In Progress

Note: Many other scenarios with varying flink configurations are under benchmarking and will be updated post completion

Secor Backups Benchmark

To ensure there is no data loss across obsrv pipeline all data is backuped to object store using S3. Following are the benchmark results of Secor backups in real-time

Configuration 1

1. Total Secor processes - 7

2. Total CPU Allocated - 1.5 cpu

3. Event size of 1 kb

Results

Events per Min	Events per hour	Events per Day	Events per process
~ 1.6 Million | 1.6Mb	~ 100 Million | 100Gb	~ 2.4 Billion | 2.4Tb	~ 300 Million | 300Gb

Note: In DIKSHA we have observed each secor process with 1cpu was able to upload 200Million events (200 Gb) to Azure blob storage

Druid Indexing Benchmark

Druid indexing benchmark is dependent on number of datasets created and number of aggregate tables. This benchmark is done with minimal configuration only and can actually linearly scale with the number of CPUs provided

Minimum Configuration

Config Name	Config Value
Process Name	Druid Indexer
CPU	0.5
Direct Memory	2Gi
Heap	9Gi
GlobalIngestionHeap	8Gi
Workers Count	30
Pod Memory	11Gi

Results

Num of Tables	Events per Min	Events per hour	Events per Day
1	~ 80k | 80 Mb	~ 4.8 Million | 4.8 Gb	~ 110 Million | 110 Gb
2	~ 40k | 40 Mb	~ 2.4 Million | 2.4 Gb	~ 55 Million | 55 Gb
3	In Progress	In Progress	In Progress
4	In Progress	In Progress	In Progress
5	~ 35k | 35 Mb	~ 2.1 Million | 2.1 Gb	~ 50 Million | 50 Gb

Note: How does the indexing scale when more cpu resources are provided will be added once the benchmark is complete

Query Benchmark

Similar to processing, query benchmark is dependent on the volume of data but not on the number of datasets (or tables) created. Query performance will increase linearly with the amount of CPU/Memory assigned to the Druid Historical process

Minimum Configuration

Config Name	Config Value
Process Name	Druid Historical
CPU	2
Direct Memory	4608Mi
Heap	1Gi
Pod Memory	5700Mi
Segment Size	4.77Gi
No. of rows per segment	5000000
processing.numThreads	2
processing.numMergeBuffers	6
Concurrency	100

RAW Table Results

Query	Query Interval	Throughput	Response Times (in ms)
Group by on Raw Data	1 Day	25 r/s	Avg | Min | Max | 90th 392 | 80 | 686 | 472
Group by on Raw Data	7 Days	4 r/s	Avg | Min | Max | 90th 4933 | 1277 | 8382 | 5154
Group by on Raw Data	30 Days	In Progress	In Progress

Aggregate (Rollup) Table Results

Query	Query Interval	Throughput	Response Times (in ms)
Group by on Aggregate Data	1 Day	In Progress	In Progress
Group by on Aggregate Data	7 Days	In Progress	In Progress
Group by on Aggregate Data	30 Days	In Progress	In Progress

Note: Multiple query types with varying interval and historical configuration combinations are being benchmarked actively and results will be updated once the activity is completed.