×
Sample configuration for AWS EC2 handling 125k Obcerv entities and 50k metrics/sec (medium) with NGINX Ingress controller
Download this sample AWS EC2 handling 125k Obcerv entities and 50k metrics/sec (medium) configuration provided by ITRS.
# Example Obcerv configuration for AWS EC2 handling 50k entities, 2M time series, and 50k metrics/sec.
#
# Nodes:
# - (3) m5.4xlarge (16 CPU, 64GiB Memory) for Timescale
# - (4) c5.4xlarge (16 CPU, 32GiB Memory) for all other workloads
#
# The resource requests for Timescale total ~24 cores and ~168GiB memory.
# The resource requests for the other workloads total ~44 cores and ~98GiB memory.
# These totals include Linkerd resources.
#
# Disk requirements:
# - Timescale:
# - 4 x 1 TiB timeseries data disk for each replica (x3)
# - 100 GiB data disk for each replica (x3)
# - 150 GiB WAL disk for each replica (x3)
# - Kafka broker: 200 GiB for each replica (x3)
# - Kafka controller: 1 GiB for each replica (x1)
# - Loki: 30 GiB
# - etcd: 1 GiB for each replica (x3)
# - Downsampled Metrics:
# - Raw: 5 GiB for each replica (x2)
# - Bucketed: 5 GiB for each replica (x2)
#
# The configuration references a default storage class named `gp3` which uses EBS gp3 volumes. This storage class should
# be configured with the default minimum gp3 settings of 3000 IOPS and 125 MiB/s throughput - you can create
# this class or change the config to use a class of your own, but it should be similar in performance.
#
# This configuration is based upon a certain number of Obcerv entities, average metrics per entity, and
# average metrics collection interval. The following function can be used to figure out what type of load to expect:
#
# metrics/sec = (Obcerv entities * metrics/entity) / average metrics collection interval
#
# In this example configuration, we have the following:
#
# 50,000 metrics/sec = (125,000 Obcerv entities * 4 metrics/entity) / 10 seconds average metrics collection interval
#
# NOTE: Ingestion, storage, and retrieval of OpenTelemetry spans is a beta feature.
#
# Additionally, the configuration is based upon a certain number of OpenTelemetry spans per second that are sampled
# based upon the following rules:
# - Error traces are always sampled
# - Target sampling probability per endpoint (corresponds to the name of the root span) is 0.01
# - Target sampling rate / second / endpoint (corresponds to the name of the root span) is 0.5
# - Root span duration outlier quantile is 0.95. The durations of all root spans are tracked and used to make guesses about
# abnormally long spans
#
defaultStorageClass: "gp3"
apps:
externalHostname: "obcerv.mydomain.internal"
ingress:
annotations:
kubernetes.io/ingress.class: "nginx"
nginx.org/mergeable-ingress-type: "master"
ingestion:
externalHostname: "obcerv-ingestion.mydomain.internal"
replicas: 2
ingress:
annotations:
kubernetes.io/ingress.class: "nginx"
nginx.ingress.kubernetes.io/backend-protocol: "GRPC"
resources:
requests:
memory: "512Mi"
cpu: "500m"
limits:
memory: "768Mi"
cpu: "1"
# NOTE: OpenTelemetry Traces ingestion is a beta feature and resources may need to be adjusted based on ingestion rate.
traces:
jvmOpts: "-Xms2G -Xmx3G"
resources:
requests:
memory: "3Gi"
cpu: "2"
limits:
memory: "4Gi"
cpu: "2500m"
iam:
ingress:
annotations:
kubernetes.io/ingress.class: "nginx"
nginx.org/mergeable-ingress-type: "minion"
kafka:
replicas: 3
diskSize: "200Gi"
resources:
requests:
memory: "6Gi"
cpu: "2"
limits:
memory: "6Gi"
cpu: "4500m"
timescale:
clusterSize: 3
dataDiskSize: "100Gi"
timeseriesDiskCount: 4
timeseriesDiskSize: "1Ti"
walDiskSize: "150Gi"
resources:
requests:
memory: "56Gi"
cpu: "8"
limits:
memory: "56Gi"
cpu: "8"
nodeSelector:
instancegroup: timescale-nodes
tolerations:
- key: dedicated
operator: Equal
value: timescale-nodes
effect: NoSchedule
retention:
entity_attributes:
chunkSize: 2d
retention: 1y
metrics:
chunkSize: 8h
retention: 30d
metrics_5m:
chunkSize: 1d
retention: 90d
metrics_1h:
chunkSize: 5d
retention: 180d
metrics_1d:
chunkSize: 20d
retention: 1y
statuses:
chunkSize: 7d
retention: 1y
signal_details:
chunkSize: 1d
retention: 30d
traces:
chunkSize: 4h
retention: 5d
# The retention for the traces table applies to this table.
span_links:
chunkSize: 1d
loki:
diskSize: "30Gi"
ingestionBurstSize: 9
ingestionRateLimit: 6
resources:
requests:
memory: "512Mi"
cpu: "250m"
limits:
memory: "512Mi"
cpu: "300m"
sinkd:
timeseriesCacheMaxSize: 2000000
replicas: 2
rawReplicas: 3
jvmOpts: "-Xms1536M -Xmx1536M -XX:MaxDirectMemorySize=100M"
rawJvmOpts: "-Xms1024M -Xmx1536M"
resources:
requests:
memory: "1536Mi"
cpu: "250m"
limits:
memory: "3Gi"
cpu: "3"
rawResources:
requests:
memory: "1280Mi"
cpu: "500m"
limits:
memory: "3Gi"
cpu: "3"
metrics:
consumerProperties:
max.partition.fetch.bytes: 524288
max.poll.records: 10000
dsMetrics:
consumerProperties:
max.partition.fetch.bytes: 1048576
loki:
consumerProperties:
max.partition.fetch.bytes: 1048576
entities:
consumerProperties:
max.partition.fetch.bytes: 1048576
signals:
consumerProperties:
max.partition.fetch.bytes: 1048576
platformd:
replicas: 2
resources:
requests:
memory: "1536Mi"
cpu: "1"
limits:
memory: "2Gi"
cpu: "2250m"
dpd:
replicas: 2
jvmOpts: "-Xmx4500M"
maxEntitySerdeCacheEntries: 50000
entitiesInMemoryCacheSizeMb: 256
consumerProperties:
max.poll.records: 10000
fetch.min.bytes: 524288
metricsMultiplexer:
maxFilterResultCacheSize: 500000
maxConcurrentOps: 500
localParallelism: 12
selfMonitoringThresholds:
metrics_partition_lag_warn: 500000
metrics_partition_lag_critical: 2500000
resources:
requests:
memory: "5Gi"
cpu: "2"
limits:
memory: "6Gi"
cpu: "4500m"
metricForecastd:
resources:
requests:
memory: "512Mi"
cpu: "250m"
limits:
memory: "768Mi"
cpu: "500m"
downsampledMetricsStream:
replicas: 2
bucketedReplicas: 2
bucketedJvmOpts: "-XX:InitialRAMPercentage=75 -XX:MaxRAMPercentage=75"
consumerProperties:
fetch.min.bytes: 524288
max.partition.fetch.bytes: 1048576
max.poll.records: 10000
resources:
requests:
memory: "3Gi"
cpu: "1"
limits:
memory: "3Gi"
cpu: "4"
bucketedConsumerProperties:
fetch.min.bytes: 524288
max.partition.fetch.bytes: 1048576
max.poll.records: 10000
bucketedResources:
requests:
memory: "3Gi"
cpu: "1"
limits:
memory: "6Gi"
cpu: "4"
rocksdb:
raw:
indexAndFilterRatio: 0.5
memoryMib: 500
writeBufferMib: 16
writeBufferRatio: 0.25
bucketed:
indexAndFilterRatio: 0.5
memoryMib: 200
writeBufferMib: 16
writeBufferRatio: 0.25
entityStream:
intermediate:
consumerProperties:
max.partition.fetch.bytes: 1048576
storedEntitiesCacheSize: 10000
replicas: 2
resources:
requests:
memory: "1536Mi"
cpu: "750m"
limits:
memory: "2Gi"
cpu: "2"
rocksdb:
memoryMib: 200
final:
jvmOpts: "-XX:InitialRAMPercentage=50 -XX:MaxRAMPercentage=50"
consumerProperties:
max.partition.fetch.bytes: 1048576
replicas: 2
storedEntitiesCacheSize: 10000
resources:
requests:
memory: "1536Mi"
cpu: "1"
limits:
memory: "2560Mi"
cpu: "2"
signalsStream:
consumerProperties:
max.partition.fetch.bytes: 1048576
resources:
requests:
memory: "830Mi"
cpu: "150m"
limits:
memory: "1536Mi"
cpu: "1200m"
etcd:
replicas: 3
collection:
daemonSet:
tolerations:
# must match the tainted Timescale nodes setting
- key: dedicated
operator: Equal
value: timescale-nodes
effect: NoSchedule
metrics:
resources:
requests:
memory: "800Mi"
cpu: "200m"
limits:
memory: "1Gi"
cpu: "500m"
["Obcerv"]
["User Guide", "Technical Reference"]