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Version: 0.7.1

Distributed search on AWS S3

In this guide, we will index about 40 million log entries (13 GB decompressed) on AWS S3 using an EC2 instance and launch a three-node distributed search cluster.

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"
  }
}
caution

Before using Quickwit with an object storage, check out our advice for deploying on AWS S3 to avoid some bad surprises at the end of the month.

First of all, let's create an EC2 instance, install a Quickwit binary, and configure it to let Quickwit access your S3 buckets. This instance will be used for indexing our dataset (note that you can also index your dataset from your local machine if it has the rights to read/write on AWS S3).

Install​

curl -L https://install.quickwit.io | sh
cd quickwit-v*/

Configure Quickwit with S3​

Let's define the S3 path where we want to store our indexes.

export S3_PATH=s3://{path/to/bucket}/indexes
note

You'll want to include the necessary authorization for the given bucket, this can be done by setting the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables, or via the AWS credentials file. Usually located at ~/.aws/credentials.

For more info check out our AWS setup guide

Now we can create a Quickwit config file.

# Create Quickwit config file.
echo "version: 0.7
node_id: searcher-1
listen_address: 0.0.0.0
metastore_uri: ${S3_PATH}
default_index_root_uri: ${S3_PATH}
" > config.yaml

You can also pass environment variables directly:

# config.yaml
node_id: searcher-1
listen_address: 0.0.0.0
version: 0.7
metastore_uri: ${S3_PATH}
default_index_root_uri: ${S3_PATH}

We are now ready to start Quickwit.

./quickwit run --config config.yaml

Create your index​

# First, download the hdfs logs config from Quickwit repository.
curl -o hdfs_logs_index_config.yaml https://raw.githubusercontent.com/quickwit-oss/quickwit/main/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_config.yaml
version: 0.7

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]

We can now create the index with the create subcommand.

./quickwit index create --index-config hdfs_logs_index_config.yaml
note

This step can also be executed on your local machine. The create command creates the index locally and then uploads a json file metastore.json to your bucket at s3://path-to-your-bucket/hdfs-logs/metastore.json.

Index logs​

The dataset is a compressed NDJSON file. Instead of downloading and indexing the data in separate steps, we will use pipes to send a decompressed stream to Quickwit directly.

wget https://quickwit-datasets-public.s3.amazonaws.com/hdfs-logs-multitenants.json.gz
gunzip -c hdfs-logs-multitenants.json.gz | ./quickwit index ingest --index hdfs-logs
note

8GB of RAM is enough to index this dataset; an instance like t4g.large with 8GB and 2 vCPU indexed this dataset in less than 10 minutes (provided that you have some CPU credits).

This step can also be done on your local machine. The ingest subcommand generates locally splits of 10 million documents and will upload them on your bucket. Concretely, each split is a bundle of index files and metadata files.

You can check it's working by using search subcommand and look for ERROR in severity_text field:

./quickwit index search --index hdfs-logs --query "severity_text:ERROR"

which returns the json

{
  "num_hits": 345,
  "hits": [
    {
      "attributes": {
        "class": "org.apache.hadoop.hdfs.server.datanode.DataNode"
      },
      "body": "RECEIVED SIGNAL 15: SIGTERM",
      "resource": {
        "service": "datanode/16"
      },
      "severity_text": "ERROR",
      "tenant_id": 51,
      "timestamp": 1469687755
    },
    ...
  ],
  "elapsed_time_micros": 522542
}

You can see that this query has 345 hits. In this case for the first run, the server responded in 523 milliseconds. Subsequent runs use the cached metastore and can be resolved in under 100 milliseconds.

Now that we have indexed the logs and can search from one instance, it's time to configure and start two other instances to form a cluster.

Start two more instances​

Quickwit needs a port rest.listen_port for serving the HTTP rest API via TCP as well as maintaining the cluster formation via UDP. Also, it needs {rest.listen_port} + 1 for gRPC communication between instances.

In AWS, you can create a security group to group these inbound rules. Check out the network section of our AWS setup guide.

To make things easier, let's create a security group that opens the TCP/UDP port range [7200-7300]. Next, create three EC2 instances using the previously created security group. Take note of each instance's public IP address.

Now ssh into the first EC2 instance, install Quickwit, and configure the environment to let Quickwit access the index S3 buckets.

Let's install Quickwit on the second and third EC2 instances.

curl -L https://install.quickwit.io | sh
cd quickwit-v*/

And configure the environment so instances can form a cluster:

export S3_PATH=s3://{path/to/bucket}/indexes
export IP_NODE_1={first-ec2-instance-public-ip}
# configuration for our second node
echo "version: 0.7
node_id: searcher-2
metastore_uri: ${S3_PATH}
default_index_root_uri: ${S3_PATH}
listen_address: 0.0.0.0
peer_seeds:
  - ${IP_NODE_1} # searcher-1
" > config.yaml

# Start a Quickwit searcher.
./quickwit run --service searcher --config config.yaml
# configuration for our third node
echo "version: 0.7
node_id: searcher-3
listen_address: 0.0.0.0
peer_seeds:
  - ${IP_NODE_1} # searcher-1
metastore_uri: ${S3_PATH}
default_index_root_uri: ${S3_PATH}
" > config.yaml

# Start a Quickwit searcher.
./quickwit run --service searcher --config config.yaml

You will see in the terminal the confirmation that the instance has joined the existing cluster. Example of such a log:

2023-03-19T16:44:56.918Z  INFO quickwit_cluster::cluster: Joining cluster. cluster_id=quickwit-default-cluster node_id=searcher-2 enabled_services={Searcher} gossip_listen_addr=0.0.0.0:7280 gossip_advertise_addr=172.31.30.168:7280 grpc_advertise_addr=172.31.30.168:7281 peer_seed_addrs=172.31.91.203:7280

Now we can query one of our instance directly by issuing http requests to one of the nodes rest API endpoint.

curl -v "http://0.0.0.0:7280/api/v1/hdfs-logs/search?query=severity_text:ERROR"

Check out the logs of all instances and you will see that all nodes are working.

Load balancing incoming requests​

Now that you have a search cluster, ideally, you will want to load balance external requests. This can quickly be done by adding an AWS load balancer to listen to incoming HTTP or HTTPS traffic and forward it to a target group. You can now play with your cluster, kill processes randomly, add/remove new instances, and keep calm.

Clean​

Let's do some cleanup by deleting the index:

./quickwit index delete --index hdfs-logs

Also remember to remove the security group to protect your EC2 instances. You can just remove the instances if you don't need them.

Congratz! You finished this tutorial!

To continue your Quickwit journey, check out the search REST API reference or the query language reference.