ElasticSearch vs Druid

Comparing two time series databases

about:nfroidure

Technical Leader @DiagRAMS
 

Building softwares for Lille's innovative startups.

Living in the Wayne's World.

 

Almost passed the Rust compiler checks once.

 

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Time Series

Series of measures representing the evolution of a value overtime

Main steps

  • Receive/pull data (FTP/HTTP/web hooks)
  • Enrich data (add labels for later aggregation)
  • Stream the data to the time series db (queueing)
  • Index the data
  • Backup raw / enriched data

Challenges

  • managing an ever growing dataset ("big data")
  • reduce data loss to its minimum
  • keep good query performances for most common queries/aggregations (cold/warm, resampling, archiving...)
  • be able to reingest the data with schema changes
  • reduce ingestion/indexing time
  • ensure ingestion reliability (message queueing)
  • ensure system availability (monitoring)

Distributed queries steps

  • Filter: select the data points
  • Map : operate on each data point
  • Reduce : compute a reduced result for each node
  • Combine : combine every nodes results

Druid

Open-source project by the Apache Software Foundation

Pros

  • Dedicated for time series
  • Open-source
  • Cheap ?
  • Fast ingestion
  • UI with ingestion overview

Cons

  • Not managed
  • Cluster need several processes
  • Relatively new
  • Less complete docs
  • No security out of the box

Elastic Search

Mature, feature complete open-source

project

Pros

  • Good performances
  • Feature complete
  • Lots of managed offers
  • Very good documentation
  • Kibana (nice datavisualization)
  • Out of the box security

Cons

  • Expensive
  • Not only for time series
  • Less efficient

Benchmarking

Working with 13G (73M points) of data on a dockerized local environment

Ingestion

  • 3h50 with ES (bulk queries via JS) :
    data is available while ingesting
    2.43Go for indice size
  • 20 minutes with Druid (local source) :
    data available only when ingestion ends
    504Mo of deep storage used

Heavy aggregation

Counting each sensors data points

Druid

# X results in 0.13s to 0.05s
SELECT "sensor", "name", SUM("count") as "count"
FROM "raw_data" GROUP BY "sensor", "name"

Elastic Search

# 120 buckets in 21.6488s to 18.571s
GET /diagrams-data-v1-2020.*/_search
{
  "query": {
    "match_all": {}
  },
  "size": 1,
  "aggs": {
    "sensors": {
      "terms": {
        "field": "sensorId",
        "size": 200
      },
      "aggs": {
        "count": {
          "value_count": {
            "field": "value"
          }
        }
      }
    }
  }
}

One sensor for one day grouped hourly

Druid

# 10 results in 0.28s to 0.10s
SELECT FLOOR("__time" to HOUR) AS HourTime,
  "sensor",  "name",  SUM("count") as "count",
  AVG("value") as "value"
FROM "raw_data"
WHERE "__time" BETWEEN
  TIMESTAMP '2020-09-15 00:00:00'
  AND TIMESTAMP '2020-09-16 00:00:00'
  AND "sensor" = '04c140cd-71b8-470f-97ac-6901007c9176'
GROUP BY 1, "sensor", "name"

Elastic Search

# 10 buckets in 0.234s to 0.070s

GET /diagrams-data-v1-2020.*/_search
{
  "query": {
    "bool": {
      "filter": [
        {
          "term": {
            "sensorId": {
              "value": "04c140cd-71b8-470f-97ac-6901007c9176"
            }
          }
        },
        {
          "range": {
            "timestamp": {
              "gte": "2020-09-15T00:00:00Z",
              "lte": "2020-09-16T00:00:00Z",
              "format": "strict_date_optional_time_nanos"
            }
          }
        }
      ]
    }
  },
  "size": 1,
  "aggs": {
    "data": {
      "date_histogram": {
        "field": "timestamp",
        "calendar_interval": "hour"
      }
    }
  }
}

One sensor for one day grouped per minutes

Druid

# 577 results in 0.17s to 0.13s
SELECT FLOOR("__time" to MINUTE) AS MinuteTime,
  "sensor",  "name",  SUM("count") as "count",
  AVG("value") as "value"
FROM "raw_data"
WHERE "__time" BETWEEN
  TIMESTAMP '2020-09-15 00:00:00'
  AND TIMESTAMP '2020-09-16 00:00:00'
  AND "sensor" = '04c140cd-71b8-470f-97ac-6901007c9176'
GROUP BY 1, "sensor", "name"

Elastic Search

# 577 buckets in 0.146 to 0.102s

GET /diagrams-data-v1-2020.*/_search
{
  "query": {
    "bool": {
      "filter": [
        {
          "term": {
            "sensorId": {
              "value": "04c140cd-71b8-470f-97ac-6901007c9176"
            }
          }
        },
        {
          "range": {
            "timestamp": {
              "gte": "2020-09-15T00:00:00Z",
              "lte": "2020-09-16T00:00:00Z",
              "format": "strict_date_optional_time_nanos"
            }
          }
        }
      ]
    }
  },
  "size": 1,
  "aggs": {
    "data": {
      "date_histogram": {
        "field": "timestamp",
        "calendar_interval": "minute"
      }
    }
  }
}

All sensors for one day grouped per minutes

Druid

# 97,823 results in 3.02s
SELECT FLOOR("__time" to MINUTE) AS MinuteTime,
  "sensor",  "name",  SUM("count") as "count",
  AVG("value") as "value"
FROM "raw_data"
WHERE "__time" BETWEEN
  TIMESTAMP '2020-09-15 00:00:00'
  AND TIMESTAMP '2020-09-16 00:00:00'
GROUP BY 1, "sensor", "name"

Elastic Search

# too_many_buckets_exception
GET /diagrams-data-v1-2020.*/_search
{
  "query": {
    "bool": {
      "filter": [
        {
          "range": {
            "timestamp": {
              "gte": "2020-09-15T00:00:00Z",
              "lte": "2020-09-16T00:00:00Z",
              "format": "strict_date_optional_time_nanos"
            }
          }
        }
      ]
    }
  },
  "size": 1,
  "aggs": {
    "data": {
      "date_histogram": {
        "field": "timestamp",
        "calendar_interval": "minute"
      },
      "aggs": {
        "sensors": {
          "terms": {
            "field": "sensorId",
            "size": 300
          }
        }
      }
    }
  }
}

Thanks! Questions?

ElasticSearch vs Druid for Time Series

By Nicolas FROIDURE

ElasticSearch vs Druid for Time Series

A little comparison between those two options for Time Series

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