Commands

Service	Port
druid-zookeeper	2181
druid-coordinator	8081
druid-overlord	8090
druid-middlemanager	8091
druid-historical	8083
druid-broker	8082
druid-router	8888
druid-postgresql	5432

DRUID_ZOOKEEPER_IP=172.18.3.2
DRUID_POSTGRESQL_IP=172.18.3.3
DRUID_COORDINATOR_IP=172.18.3.4
DRUID_HISTORICAL_IP=172.18.3.5
DRUID_BROKER_IP=172.18.3.6
DRUID_INIT_IP=172.18.3.6
DRUID_OVERLORD_IP=172.18.3.7
DRUID_MIDDLEMANAGER_IP=172.18.3.8
DRUID_ROUTER_IP=172.18.3.15

Hack

docker exec -it druid-historical bash
mkdir /var/druid/tmp

APIs

Historical

http://localhost:8083/druid/historical/v1/readiness

http://localhost:8083/druid/historical/v1/loadstatus

http://localhost:8083/status

Druid segment cleanup

docker exec -it druid-historical bash

## # first remove segment-cache and then segments

cd /var/druid/segment-cache and cd /var/druid/segments
remove last 20 days of segments

Druid Commands

#query top pages from wikipedia dataset
curl -X 'POST' -H 'Content-Type:application/json' -d @wikipedia-top-pages.json [http://localhost:8082/druid/v2?pretty](http://localhost:8082/druid/v2/?pretty)

#submit supervisor spec to kafka-indexing-service
curl -XPOST -H'Content-Type: application/json' -d @wikipedia-kafka-supervisor.json http://localhost:8090/druid/indexer/v1/supervisor

curl -XPOST -H'Content-Type: application/json' -d @smap-kafka-supervisor-spec.json http://localhost:8090/druid/indexer/v1/supervisor

Dashboards

• 8081: coordinator (for seeing clusters and datasources
• 8090: overlord (for managing supervisor spec and tasks)
• Others
  • 8082: broker
  • 8083: historical
  • 8091: middlemanager
  • 2181: zookeeper

Debugging

stop druid-historical
cd /var/lib/docker/volumes/druid-volume/_data/segment-cache
remove docker segment-cache
rm -rf *
start druid-historical

SQL Commands

# find duplicate count
SELECT controller_name, site_name, count(reading) as DuplicateCount FROM (SELECT controller_name, stream_path, site_name, reading, __time, count(reading) as readingCount FROM "live-Samhi"
  WHERE __time BETWEEN TIMESTAMP '2019-11-18 08:00:00' AND TIMESTAMP '2019-11-19 08:00:00'
  AND stream_path LIKE '/Samhi-41%'
  GROUP BY __time, stream_path, reading, site_name, controller_name
  HAVING readingCount > 1
  ORDER BY __time DESC)
  GROUP BY site_name, controller_name

# find duplicate values
SELECT controller_name, stream_path, site_name, reading, __time, count(reading) as readingCount FROM "live-Samhi"
  WHERE __time BETWEEN TIMESTAMP '2019-11-18 08:00:00' AND TIMESTAMP '2019-11-19 09:00:00'
  AND stream_path LIKE '/Samhi-41%'
  GROUP BY __time, stream_path, reading, site_name, controller_name
  HAVING readingCount > 1
ORDER BY __time DESC

Dashboard

http://10.9.1.21:8888/unified-console.html

Ingestion Spec

{
  "type": "kafka",
  "dataSchema": {
    "dataSource": "live-Samhi",
    "parser": {
      "type": "string",
      "parseSpec": {
        "format": "json",
        "timestampSpec": {
          "column": "time",
          "format": "auto"
        },
        "dimensionsSpec": {
          "spatialDimensions": [
            {
              "dimName": "coordinates",
              "dims": [
                "site_latitude",
                "site_longitude"
              ]
            }
          ],
          "dimensions": [
            "uuid",
            "path",
            {
              "name": "reading",
              "type": "float"
            },
            "stream_path",
            "stream_uuid",
            "device_tags",
            "device_is_virtual",
            "device_id",
            "device_display_name",
            "device_path",
            "device_type",
            "device_category",
            "physical_parameter_display",
            "physical_parameter_unit",
            "physical_parameter_type",
            "physical_parameter_name",
            "site_longitude",
            "site_latitude",
            "site_name",
            "customer_name",
            "client_name",
            "controller_name",
            "site_Size",
            "site_Floors",
            "site_site_code",
            "site_Hotel_Type",
            "site_Display_Name",
            "metric_tod_metadata",
            "metric_operational_metadata",
            "live_parent",
            "live_region",
            "live_purpose",
            "live_pie_breakup",
            "live_room_number",
            "live_room_direction"
          ]
        }
      }
    },
    "metricsSpec": [
      {
        "type": "count",
        "name": "count"
      }
    ],
    "granularitySpec": {
      "type": "uniform",
      "segmentGranularity": "HOUR",
      "rollup": false
    }
  },
  "tuningConfig": {
    "type": "kafka",
    "maxSavedParseExceptions": 1000,
    "forceExtendableShardSpecs": true
  },
  "ioConfig": {
    "topic": "druid_telemetry_data_Samhi",
    "taskCount": 1,
    "replicas": 1,
    "taskDuration": "PT120S",
    "completionTimeout": "PT5M",
    "useEarliestOffset": true,
    "consumerProperties": {
      "bootstrap.servers": "kafka0.zenatix.com:31090,kafka1.zenatix.com:31091,kafka2.zenatix.com:31092"
    }
  }
}

Important Points

• Compression - 1:10
• Number of hyper threads
• Concurrency of queries
  • 4 gb/s per hyperthread - how much data you are consuming
• Servers
  • I3.extra large - 3 data server
  • 1 middlemaanger
  • 2 query server - m5 large
  • Master - m5 large
  • m4.xlarge