How to update a JSON index

In this recipe we'll learn how to update the configuration of a JSON index.

Pinot Version	1.0.0
Code	startreedata/pinot-recipes/update-json-index

Prerequisites

To follow the code examples in this guide, you must install Docker (opens in a new tab) locally and download recipes.

Navigate to recipe

If you haven't already, download recipes.
In terminal, go to the recipe by running the following command:

cd pinot-recipes/recipes/update-json-index

Launch Pinot Cluster

You can spin up a Pinot Cluster by running the following command:

docker-compose up

This command will run a single instance of the Pinot Controller, Pinot Server, Pinot Broker, Kafka, and Zookeeper. You can find the docker-compose.yml (opens in a new tab) file on GitHub.

Data generator

This recipe contains a data generator that creates events with data about people.

It uses the Faker library (opens in a new tab), so you'll first need to install that:

pip install faker

You can generate data by running the following command:

python datagen.py 2>/dev/null  | head -n1 | jq

Output is shown below:

{
  "ts": 1680172162791,
  "person": {
    "id": "ead572cb-c8bc-457a-b331-4f380c5ebe18",
    "name": "Melissa Howard",
    "email": "daniel75@example.com",
    "age": 33,
    "address": {
      "street_address": "974 Michael Hollow",
      "city": "Johnstonport",
      "state": "South Dakota",
      "country": "Latvia"
    },
    "phone_number": "+1-697-077-6893x6562",
    "job": {
      "company": "Ramirez-Hunt",
      "position": "Communications engineer",
      "department": "iterate efficient e-markets"
    },
    "interests": [
      "Swimming"
    ],
    "friend_ids": [
      "88a696de-34d1-4300-ac3c-3084a6bdceeb"
    ]
  }
}

Kafka ingestion

We're going to ingest this data into an Apache Kafka topic using the kcat (opens in a new tab) command line tool. We'll also use jq to structure the data in the key:payload structure that Kafka expects:

python datagen.py --sleep 0.0001 2>/dev/null |
jq -cr --arg sep ø '[.uuid, tostring] | join($sep)' |
kcat -P -b localhost:9092 -t people -Kø

We can check that Kafka has some data by running the following command:

docker exec -it kafka-querysegment kafka-run-class.sh \
  kafka.tools.GetOffsetShell \
  --broker-list localhost:9092 \
  --topic events

We'll see something like the following:

events:0:19960902

Pinot Schema and Table

Now let's create a Pinot Schema and Table.

First, the schema:

{
    "schemaName": "people",
    "dimensionFieldSpecs": [
      {
        "name": "person",
        "dataType": "JSON"
      }
    ],
    "metricFieldSpecs": [],
    "dateTimeFieldSpecs": [
        {
            "name": "ts",
            "dataType": "TIMESTAMP",
            "format": "1:MILLISECONDS:EPOCH",
            "granularity": "1:MILLISECONDS"
          }
    ]
  }

schema.json

Our schema has only two columns - one for the timestamp and another one that stores the person.

Now for the table config:

{
    "tableName": "people",
    "tableType": "REALTIME",
    "segmentsConfig": {
      "timeColumnName": "ts",
      "schemaName": "people",
      "replication": "1",
      "replicasPerPartition": "1"
    },
    "tableIndexConfig": {
      "loadMode": "MMAP",
      "jsonIndexConfigs": {
        "person": {
          "maxLevels": 2,
          "excludeArray": false,
          "disableCrossArrayUnnest": true,
          "includePaths": null,
          "excludePaths": null,
          "excludeFields": null
        }
      },
      "streamConfigs": {
        "streamType": "kafka",
        "stream.kafka.topic.name": "people",
        "stream.kafka.broker.list": "kafka-jsonindex:9093",
        "stream.kafka.consumer.type": "lowlevel",
        "stream.kafka.consumer.prop.auto.offset.reset": "smallest",
        "stream.kafka.consumer.factory.class.name": "org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory",
        "stream.kafka.decoder.class.name": "org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder",
        "realtime.segment.flush.threshold.rows":"100000",
        "realtime.segment.flush.threshold.time":"1h"
      }
    },
    "ingestionConfig": {
       
      },
    "tenants": {},
    "metadata": {}
  }

table.json

This highlighted section contains the config for the JSON index. An explanation of each of the config parameters is shown below:

Config Key	Description	Type	Default
maxLevels	Max levels to flatten the json object (array is also counted as one level)	`int`	-1 (unlimited)
excludeArray	Whether to exclude array when flattening the object	`boolean`	false (include array)
disableCrossArrayUnnest	Whether to not unnest multiple arrays (unique combination of all elements)	`boolean`	false (calculate unique combination of all elements)
includePaths	Only include the given paths, e.g. "$.a.b", "$.a.c[]" (mutual exclusive with excludePaths). Paths under the included paths will be included, e.g. "$.a.b.c" will be included when "$.a.b*" is configured to be included.	`Set<String\>`	null (include all paths)
excludePaths	Exclude the given paths, e.g. "$.a.b", "$.a.c[]" (mutual exclusive with includePaths). Paths under the excluded paths will also be excluded, e.g. "$.a.b.c" will be excluded when "$.a.b*" is configured to be excluded.	`Set<String\>`	null (include all paths)
excludeFields	Exclude the given fields, e.g. "b", "c", even if it is under the included paths.	`Set<String\>`	null (include all fields)

So we are including all fields in the JSON index.

💡

Fields included in the JSON index can be filtered using the JSON_MATCH function. If you use this function with a field that isn't included in the index, it won't return any records.

We'll create the table by running the following:

docker run \
   --network jsonindex \
   -v $PWD/config:/config \
   apachepinot/pinot:1.0.0 AddTable \
     -schemaFile /config/schema.json \
     -tableConfigFile /config/table.json \
     -controllerHost "pinot-controller-jsonindex" \
    -exec

Querying by JSON index

Let's now try to query this table using the JSON index. JSON indexes support the following predicates: =, <>, IN, and NOT IN

select json_extract_scalar(person, '$.address.state', 'STRING') AS state, count(*)
from people 
WHERE JSON_MATCH(person, '"$.age" = 59')
GROUP BY state
ORDER BY count(*) DESC

state	count(*)
New Hampshire	34
Pennsylvania	32
Kansas	32
New Jersey	32
Maryland	31
Vermont	30
Rhode Island	30
Virginia	30
Ohio	30
Washington	29

Query Results

And one more:

select count(*)
from people 
WHERE JSON_MATCH(person, '"$.interests[0]" = ''Swimming''')

count(*)
3319

Query Results

Updating JSON index

Now let's say we want to update the config of the JSON index so that not all fields are indexed. At the moment this must be done in the following steps:

Update the table config to remove the index completely.
Refresh all segments.
Update the table config with the new config.
Refresh all segments.

Let's first remove the index by calling the AddTable command with the following table config:

{
    "tableName": "people",
    "tableType": "REALTIME",
    "segmentsConfig": {
      "timeColumnName": "ts",
      "schemaName": "people",
      "replication": "1",
      "replicasPerPartition": "1"
    },
    "tableIndexConfig": {
      "loadMode": "MMAP",
      "streamConfigs": {
        "streamType": "kafka",
        "stream.kafka.topic.name": "people",
        "stream.kafka.broker.list": "kafka-jsonindex:9093",
        "stream.kafka.consumer.type": "lowlevel",
        "stream.kafka.consumer.prop.auto.offset.reset": "smallest",
        "stream.kafka.consumer.factory.class.name": "org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory",
        "stream.kafka.decoder.class.name": "org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder",
        "realtime.segment.flush.threshold.rows":"100000",
        "realtime.segment.flush.threshold.time":"1h"
      }
    },
    "ingestionConfig": {
       
      },
    "tenants": {},
    "metadata": {}
  }

table-no-index.json

Next, let's run the command

docker run \
   --network jsonindex \
   -v $PWD/config:/config \
   apachepinot/pinot:1.0.0 AddTable \
     -schemaFile /config/schema.json \
     -tableConfigFile /config/table-no-index.json \
     -controllerHost "pinot-controller-jsonindex" \
    -exec -update

And let's refresh all segments:

curl -X 'POST' 'http://localhost:9000/segments/people_REALTIME/reload?type=REALTIME'

If we now re-run our last query, we'll get the following output:

[
  {
    "message": "QueryExecutionError:\njava.lang.IllegalStateException: Cannot apply JSON_MATCH on column: person without json index\n\tat org.apache.pinot.shaded.com.google.common.base.Preconditions.checkState(Preconditions.java:518)\n\tat org.apache.pinot.core.plan.FilterPlanNode.constructPhysicalOperator(FilterPlanNode.java:285)\n\tat org.apache.pinot.core.plan.FilterPlanNode.run(FilterPlanNode.java:92)\n\tat org.apache.pinot.core.plan.AggregationPlanNode.buildNonFilteredAggOperator(AggregationPlanNode.java:178)",
    "errorCode": 200
  }
]

Let's now add the updated index, based on the table config below:

{
    "tableName": "people",
    "tableType": "REALTIME",
    "segmentsConfig": {
      "timeColumnName": "ts",
      "schemaName": "people",
      "replication": "1",
      "replicasPerPartition": "1"
    },
    "tableIndexConfig": {
      "loadMode": "MMAP",
      "jsonIndexConfigs": {
        "person": {
          "maxLevels": 2,
          "excludeArray": true,
          "disableCrossArrayUnnest": true,
          "includePaths": null,
          "excludePaths": ["$.address.street_address"],
          "excludeFields": ["age"]
        }
      },
      "streamConfigs": {
        "streamType": "kafka",
        "stream.kafka.topic.name": "people",
        "stream.kafka.broker.list": "kafka-jsonindex:9093",
        "stream.kafka.consumer.type": "lowlevel",
        "stream.kafka.consumer.prop.auto.offset.reset": "smallest",
        "stream.kafka.consumer.factory.class.name": "org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory",
        "stream.kafka.decoder.class.name": "org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder",
        "realtime.segment.flush.threshold.rows":"100000",
        "realtime.segment.flush.threshold.time":"1h"
      }
    },
    "ingestionConfig": {
       
      },
    "tenants": {},
    "metadata": {}
  }

table-updated-index.json

This time we're not going to index age, any array fields (i.e interests or friend_ids), or address.street_address.

Let's apply that config

docker run \
   --network jsonindex \
   -v $PWD/config:/config \
   apachepinot/pinot:1.0.0 AddTable \
     -schemaFile /config/schema.json \
     -tableConfigFile /config/table-updated-index.json \
     -controllerHost "pinot-controller-jsonindex" \
    -exec -update

Refresh all segments again:

curl -X 'POST' 'http://localhost:9000/segments/people_REALTIME/reload?type=REALTIME'

It's query time! Let's start with the swimming query:

select count(*)
from people 
WHERE JSON_MATCH(person, '"$.interests[0]" = ''Swimming''')

count(*)
0

Query Results

This field isn't indexed anymore, so we don't get any results.

Same thing if we search by street address:

select count(*)
from people 
WHERE JSON_MATCH(person, '"$.address.street_address" = ''862 Mercado Mountain Suite 529''')

count(*)
0

Query Results

But we can still search by country:

select count(*)
from people 
WHERE JSON_MATCH(person, '"$.address.country" = ''Croatia''')

count(*)
308

Query Results

Indexing JSON columns Pause and Resume Consumption