How to index a JSON column
In this recipe we'll learn how to configure an index for a JSON column.
Pinot Version | 0.12.0 |
Code | startreedata/pinot-recipes/json-index |
Pre-requisites
You will need to install Docker to follow the code examples in this guide.
Download Recipe
First, clone the GitHub repository to your local machine and navigate to this recipe:
git clone git@github.com:startreedata/pinot-recipes.git
cd pinot-recipes/recipes/json-index
If you don't have a Git client, you can also download a zip file that contains the code and then navigate to the recipe.
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 file on GitHub.
Data generator
This recipe contains a data generator that creates events with data about people.
It uses the Faker library, 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 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"
}
]
}
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": ["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": {}
}
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 except age
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 $PWD/config:/config \
apachepinot/pinot:0.12.0-arm64 AddTable \
-schemaFile /config/schema.json \
-tableConfigFile /config/table.json \
-controllerHost "pinot-controller-jsonindex" \
-exec
Querying by JSON index
Let's now try to query this table using the JSON index.
JSON indexes support the following predicates: =
, <>
, IN
, and NOT IN
State = Kentucky
select ts, person
from people
WHERE JSON_MATCH(person, '"$.address.state" = ''Kentucky''')
LIMIT 3
ts | person |
---|---|
2023-03-30 09:40:59.788 | {"address":{"street_address":"8173 Diaz Hollow Apt. 175","country":"British Indian Ocean Territory (Chagos Archipelago)","city":"East Carlos","state":"Kentucky"},"friend_ids":["de27ec51-2895-4044-98bc-5643853d7d0d","84080eb3-1c17-4b4a-ada7-a764ed44cef7"],"name":"Robert Alvarado","phone_number":"085.136.9039x6810","id":"874a4543-f528-49e2-85a8-d31a6cbae269","job":{"company":"Gray, Huerta and Moyer","position":"Chemical engineer","department":"transition collaborative networks"},"interests":["Swimming","Meditation","Reading","Cycling","Reading"],"email":"jamiebarker@example.net","age":52} |
2023-03-30 09:40:59.823 | {"address":{"street_address":"277 Robert Crossroad Apt. 622","country":"Haiti","city":"Port Karenshire","state":"Kentucky"},"friend_ids":["2285322d-0049-4386-b41c-9f96a0ed5038"],"name":"Diana Thomas","phone_number":"171-966-4122","id":"b996770e-478c-412d-89bd-d3a085140016","job":{"company":"Diaz LLC","position":"Scientist, research (maths)","department":"deploy granular convergence"},"interests":["Sports","Photography","Art","Photography","Reading"],"email":"ncook@example.org","age":40} |
2023-03-30 09:40:59.843 | {"address":{"street_address":"85628 William Curve","country":"Djibouti","city":"Gregoryborough","state":"Kentucky"},"friend_ids":["1a7ee9e4-6daa-4994-a077-3170bffab04f"],"name":"Monica Harrell","phone_number":"(120)253-7632","id":"c71df52f-49c4-4d61-b6d6-d94134c92e8b","job":{"company":"Larson-Mcdaniel","position":"Ranger/warden","department":"seize leading-edge web services"},"interests":["Traveling","Music","Art"],"email":"allen60@example.net","age":33} |
Query Results
State <> Kentucky
select count(*)
from people
WHERE JSON_MATCH(person, '"$.address.state" <> ''Kentucky''')
count(*) |
---|
319584 |
Query Results
State IN Kentucky or Alabama
select json_extract_scalar(person, '$.address.state', 'STRING') AS state, count(*)
from people
WHERE JSON_MATCH(person, '"$.address.state" IN (''Kentucky'', ''Alabama'')')
GROUP BY state
ORDER BY count(*) DESC
state | count(*) |
---|---|
Kentucky | 6682 |
Alabama | 6414 |
Query Results
State NOT IN Kentucky or Alabama
select json_extract_scalar(person, '$.address.state', 'STRING') AS state, count(*)
from people
WHERE JSON_MATCH(person, '"$.address.state" NOT IN (''Kentucky'', ''Alabama'')')
GROUP BY state
ORDER BY count(*) DESC
state | count(*) |
---|---|
Montana | 6743 |
Kansas | 6718 |
Maryland | 6693 |
Ohio | 6677 |
Connecticut | 6662 |
Mississippi | 6639 |
Rhode Island | 6630 |
Arkansas | 6621 |
Query Results
Unindexed field
What if we try to query by the age
column, which hasn't been indexed?
select count(*)
from people
WHERE JSON_MATCH(person, '"$.age"=59')
count(*) |
---|
0 |
Query Results
We get back no results! We can even run the following query to confirm that the table contains records with an age of 59:
select count(*)
from people
WHERE JSON_EXTRACT_SCALAR(person, '$.age', 'STRING') = 59
count(*) |
---|
5018 |
Query Results