Index a logging dataset locally

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In this guide, we will index about 20 million log entries (7 GB decompressed) on a local machine. If you want to start a server with indexes on AWS S3 with several search nodes, check out the tutorial for distributed search.

Here is an example of a log entry:

{
  "timestamp": 1460530013,
  "severity_text": "INFO",
  "body": "PacketResponder: BP-108841162-10.10.34.11-1440074360971:blk_1074072698_331874, type=HAS_DOWNSTREAM_IN_PIPELINE terminating",
  "resource": {
    "service": "datanode/01"
  },
  "attributes": {
    "class": "org.apache.hadoop.hdfs.server.datanode.DataNode"
  },
  "tenant_id": 58
}

Let's download and install Quickwit.

curl -L https://raw.githubusercontent.com/quickwit-oss/quickwit/main/install.sh | sh
cd quickwit-v*/

Or pull and run the Quickwit binary in an isolated Docker container.

docker run quickwit/quickwit:0.9.0 --version
  • CLI
  • Docker
./quickwit run

docker run --rm -v $(pwd)/qwdata:/quickwit/qwdata -p 127.0.0.1:7280:7280 quickwit/quickwit:0.9.0 run

Let's create an index configured to receive these logs.

## First, download the hdfs logs config from Quickwit repository.
curl -o hdfs_logs_index_config.yaml https://raw.githubusercontent.com/quickwit-oss/quickwit/v0.9.0/config/tutorials/hdfs-logs/index-config.yaml

The index config defines five fields: timestamp, tenant_id, severity_text, body, and one JSON field for the nested values resource.service, we could use an object field here and maintain a fixed schema, but for convenience we're going to use a JSON field. It also sets the default_search_fields, the tag_fields, and the timestamp_field. The timestamp_field and tag_fields are used by Quickwit for splits pruning at query time to boost search speed. Check out the index config docs for more details.

hdfs-logs-index.yaml

version: 0.8

index_id: hdfs-logs

doc_mapping:
  field_mappings:
    - name: timestamp
      type: datetime
      input_formats:
        - unix_timestamp
      output_format: unix_timestamp_secs
      fast_precision: seconds
      fast: true
    - name: tenant_id
      type: u64
    - name: severity_text
      type: text
      tokenizer: raw
    - name: body
      type: text
      tokenizer: default
      record: position
    - name: resource
      type: json
      tokenizer: raw
  tag_fields: [tenant_id]
  timestamp_field: timestamp

search_settings:
  default_search_fields: [severity_text, body]

Now let's create the index with the create subcommand (assuming you are inside Quickwit install directory):

  • CLI
  • cURL
./quickwit index create --index-config hdfs_logs_index_config.yaml

curl -XPOST http://localhost:7280/api/v1/indexes -H "content-type: application/yaml" --data-binary @hdfs_logs_index_config.yaml

You're now ready to fill the index.

The dataset is a compressed NDJSON file. Instead of downloading it and then indexing the data, we will use pipes to directly send a decompressed stream to Quickwit. This can take up to 10 minutes on a modern machine, the perfect time for a coffee break.

  • CLI
  • Docker
curl https://quickwit-datasets-public.s3.amazonaws.com/hdfs-logs-multitenants.json.gz | gunzip | ./quickwit index ingest --index hdfs-logs --force

On macOS or Windows:

curl https://quickwit-datasets-public.s3.amazonaws.com/hdfs-logs-multitenants.json.gz | gunzip | docker run -v $(pwd)/qwdata:/quickwit/qwdata -i quickwit/quickwit:0.9.0 index ingest --index hdfs-logs --force --endpoint http://host.docker.internal:7280

On Linux:

curl https://quickwit-datasets-public.s3.amazonaws.com/hdfs-logs-multitenants.json.gz | gunzip | docker run --network=host -v $(pwd)/qwdata:/quickwit/qwdata -i quickwit/quickwit:0.9.0 index ingest --index hdfs-logs --force --endpoint http://127.0.0.1:7280

If you are in a hurry, use the sample dataset that contains 10 000 documents, we will use this dataset for the example queries:

  • CLI
  • Docker
  • cURL
curl https://quickwit-datasets-public.s3.amazonaws.com/hdfs-logs-multitenants-10000.json | ./quickwit index ingest --index hdfs-logs --force

On macOS or Windows:

curl https://quickwit-datasets-public.s3.amazonaws.com/hdfs-logs-multitenants-10000.json | docker run -v $(pwd)/qwdata:/quickwit/qwdata -i quickwit/quickwit:0.9.0 index ingest --index hdfs-logs --force --endpoint http://host.docker.internal:7280

On linux:

curl https://quickwit-datasets-public.s3.amazonaws.com/hdfs-logs-multitenants-10000.json | docker run --network=host -v $(pwd)/qwdata:/quickwit/qwdata -i quickwit/quickwit:0.9.0 index ingest --index hdfs-logs --force --endpoint http://127.0.0.1:7280

wget https://quickwit-datasets-public.s3.amazonaws.com/hdfs-logs-multitenants-10000.json
curl -XPOST "http://localhost:7280/api/v1/hdfs-logs/ingest?commit=force" -H "content-type: application/json" --data-binary @hdfs-logs-multitenants-10000.json

You can check it's working by searching for INFO in severity_text field:

  • CLI
  • Docker
./quickwit index search --index hdfs-logs  --query "severity_text:INFO"

On macOS or Windows:

docker run -v $(pwd)/qwdata:/quickwit/qwdata quickwit/quickwit:0.9.0 index search --index hdfs-logs --query "severity_text:INFO" --endpoint http://host.docker.internal:7280

On linux:

docker run --network=host -v $(pwd)/qwdata:/quickwit/qwdata quickwit/quickwit:0.9.0 index search --index hdfs-logs --query "severity_text:INFO" --endpoint http://127.0.0.1:7280

note

The ingest subcommand generates splits of 5 million documents. Each split is a small piece of index represented by a file in which index files and metadata files are saved.

The query which returns the json:

{
  "num_hits": 10000,
  "hits": [
    {
      "body": "Receiving BP-108841162-10.10.34.11-1440074360971:blk_1073836032_95208 src: /10.10.34.20:60300 dest: /10.10.34.13:50010",
      "resource": {
        "service": "datanode/03"
      },
      "severity_text": "INFO",
      "tenant_id": 58,
      "timestamp": 1440670490
    }
    ...
  ],
  "elapsed_time_micros": 2490
}

The index config shows that we can use the timestamp field parameters start_timestamp and end_timestamp and benefit from time pruning. Behind the scenes, Quickwit will only query splits that have logs in this time range.

Let's use these parameters with the following query:

curl 'http://127.0.0.1:7280/api/v1/hdfs-logs/search?query=severity_text:INFO&start_timestamp=1440670490&end_timestamp=1450670490'

Let's do some cleanup by deleting the index:

  • CLI
  • cURL
./quickwit index delete --index hdfs-logs

curl -XDELETE http://127.0.0.1:7280/api/v1/indexes/hdfs-logs

Congratulations! You finished this tutorial!

To continue your Quickwit journey, check out the tutorial for distributed search or dig into the search REST API or query language.