![[Trino] MongoDB connector, Kafka event Listener 적용하기](https://img1.daumcdn.net/thumb/R750x0/?scode=mtistory2&fname=https%3A%2F%2Fblog.kakaocdn.net%2Fdna%2FbMGrWY%2FbtsOGq2zxpk%2FAAAAAAAAAAAAAAAAAAAAALAzY2TwcJwvh-1Xa3JzEDCOXWR4nfKRkpYUlsZR7iP2%2Fimg.png%3Fcredential%3DyqXZFxpELC7KVnFOS48ylbz2pIh7yKj8%26expires%3D1777561199%26allow_ip%3D%26allow_referer%3D%26signature%3Ds5KTKcPQ76z%252B7U2seXAQdWoW0RQ%253D)
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이번 포스팅에서는 Trino에서 MongoDB Connector와 Kafka event Listener를 연결 및 적용해보려고 합니다.
프로젝트에서 MongoDB와 Kafka를 사용하고 있어서, 당장은 아니지만 나중에 필요할 수도 있어 기록차 남기려고 합니다.
실습환경은 Docker를 사용하였습니다.
Docker Compose
사용한 docker-compose.yml은 아래와 같습니다.
services:
mongodb:
container_name: mongodb
image: mongo:6.0.24
environment:
MONGO_INITDB_ROOT_USERNAME: manager
MONGO_INITDB_ROOT_PASSWORD: manager
ports:
- "27017:27017"
kafka1:
image: bitnami/kafka:3.6.1
container_name: kafka1
ports:
- 9092:9092
environment:
KAFKA_BROKER_ID: 1
ALLOW_PLAINTEXT_LISTENER: "yes"
KAFKA_ZOOKEEPER_CONNECT: "zookeeper1:2181"
KAFKA_LISTENERS: PLAINTEXT://kafka1:9092
KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://10.198.64.5:9092
depends_on:
- zookeeper1
zookeeper1:
image: bitnami/zookeeper:3.8.3
container_name: zookeeper1
ports:
- 2181:2181
environment:
ALLOW_ANONYMOUS_LOGIN: "yes"
kafka-ui:
image: provectuslabs/kafka-ui:latest
container_name: kafka-ui
depends_on:
- kafka1
ports:
- 8088:8080
environment:
- DYNAMIC_CONFIG_ENABLED=true
- KAFKA_CLUSTERS_0_NAME=kafka1
- KAFKA_CLUSTERS_0_BOOTSTRAPSERVERS=kafka1:9092
trino-coordinator:
container_name: trino-coordinator
image: trinodb/trino:464
ports:
- 8080:8080
volumes:
- ./coordinator/config.properties:/etc/trino/config.properties
- ./catalogs/mongodb.properties:/etc/trino/catalog/mongodb.properties
- ./event-listener/kafka-event-listener.properties:/etc/trino/event-listener/kafka-event-listener.properties
healthcheck:
test: ["CMD", "/usr/lib/trino/bin/health-check"]
interval: 10s
timeout: 30s
retries: 10
depends_on:
- mongodb
- kafka1
trino-worker:
container_name: trino-worker
image: trinodb/trino:464
volumes:
- ./worker/config.properties:/etc/trino/config.properties
- ./catalogs/mongodb.properties:/etc/trino/catalog/mongodb.properties
- ./event-listener/kafka-event-listener.properties:/etc/trino/event-listener/kafka-event-listener.properties
depends_on:
- trino-coordinator1. mongodb
2. kafka & zookeeper & kafka-ui
3. trino-coordinator & trino-worker
필요한 애플리케이션을 작성하고 필요에 따라 depends_on을 추가로 걸어주었습니다.
Trino 설정
Trino와 MongoDB, Kafka를 연결하기위하여 Trino에 설정해야 할 것들이 있습니다.
config.properties
//COORDINATOR
coordinator=true
node-scheduler.include-coordinator=false
http-server.http.port=8080
catalog.management=${ENV:CATALOG_MANAGEMENT}
discovery.uri=http://trino-coordinator:8080
event-listener.config-files=/etc/trino/event-listener/kafka-event-listener.properties
-------------------
//WORKER
coordinator=false
node-scheduler.include-coordinator=true
http-server.http.port=8080
catalog.management=${ENV:CATALOG_MANAGEMENT}
event-listener.config-files=/etc/trino/event-listener/kafka-event-listener.properties
#authentication
discovery.uri=http://trino-coordinator:8080기본적인 설정과 함께 event-listener.config-files의 경로를 지정해주었습니다.
Kafka-event-listener
event-listener.name=kafka
kafka-event-listener.broker-endpoints=kafka1:9092
kafka-event-listener.created-event.topic=my_first_event_listener.created_event
kafka-event-listener.completed-event.topic=my_first_event_listener.completed_event
kafka-event-listener.client-id=my_trino_event_listenerkafak-event-listener의 설정값입니다.
name, broker 연결정보, create-event 토픽, completed-event 토픽의 이름을 지정해줍니다.
kafka-ui를 통하여 확인하면, 지정한 이름을 토픽명으로 사용한 것을 확인할 수 있습니다.
MongoDB Connector
connector.name=mongodb
mongodb.connection-url=mongodb://ID:PASSWORD@MONGOIP:MONGOPORTmongodb.properties의 설정값입니다.
위 설정으로 Docker container를 실행하고 MongoDB의 데이터베이스와 콜렉션을 생성 후 조회를 해보았습니다.
SELECT
위처럼 플랫한 형태의 데이터를 넣어두고 Dbeaver를 조회해보았습니다.
일반 테이블 조회하듯 잘 조회되는 것을 볼 수 있습니다.
Nested한 JSON의 형태도 확인해보았습니다.
{
"user_id": 1,
"name": "Alice",
"email": "alice@example.com",
"signup_date": "2023-01-15",
"address": {
"street": "123 Maple Street",
"city": "Wonderland",
"zip": "12345"
},
"orders": [
{
"order_id": 1001,
"date": "2023-02-01",
"items": [
{ "product": "Book", "qty": 2, "price": 12.99 },
{ "product": "Pen", "qty": 5, "price": 1.5 }
]
},
{
"order_id": 1002,
"date": "2023-03-10",
"items": [
{ "product": "Notebook", "qty": 1, "price": 5.99 }
]
}
]
}
select address, address.street,address.city from mydb.nestedcollection;
address와 같은 key:value 형태의 데이터 잘 조회되는 것을 볼 수 있습니다.
address.street, address.city 처럼 .을 붙어 중첩된 필드를 접근해서 확인 할 수 있습니다.
orders와 같은 ARRAY<ROW>(배열) 또한 아래와 같은 쿼리로 조회 할 수 있습니다.
SELECT
u.user_id,
t.order_id
FROM mongodb.mydb.nestedcollection u
CROSS JOIN UNNEST(u.orders) AS t;
SELECT
u.user_id,
order_id,
date,
items
FROM mongodb.mydb.nestedcollection u
CROSS JOIN UNNEST(u.orders) AS t(order_id, date, items);
Event Listener(Kafka)
위 처럼 Trino에 쿼리를 전송하여 데이터를 조회했을때 event listener에는 어떤식으로 저장이 되는지 확인해보겠습니다.
CREATE EVENT
{
"eventPayload": {
"createTime": "2025-06-17T13:30:15.809Z",
"context": {
"user": "admin",
"originalUser": "admin",
"principal": "admin",
"enabledRoles": [],
"groups": [],
"remoteClientAddress": "******",
"userAgent": "Trino JDBC Driver/462",
"clientTags": [],
"clientCapabilities": [
"PATH",
"PARAMETRIC_DATETIME",
"SESSION_AUTHORIZATION"
],
"source": "DBeaver 24.2.1 - SQLEditor <Script-17.sql>",
"timezone": "Asia/Seoul",
"catalog": "mongodb",
"resourceGroupId": [
"global"
],
"sessionProperties": {},
"resourceEstimates": {},
"serverAddress": "*******",
"serverVersion": "464",
"environment": "docker",
"queryType": "SELECT",
"retryPolicy": "NONE"
},
"metadata": {
"queryId": "20250617_133015_00113_w86kp",
"transactionId": "001b8117-00b5-48ee-ae27-01f318902c73",
"query": "SELECT\r\n u.user_id,\r\n order_id,\r\n date,\r\n items\r\nFROM mongodb.mydb.nestedcollection u\r\nCROSS JOIN UNNEST(u.orders) AS t(order_id, date, items)\nLIMIT 200",
"queryState": "QUEUED",
"tables": [],
"routines": [],
"uri": "http://******:8080/v1/query/20250617_133015_00113_w86kp"
}
},
"eventMetadata": {}
}각 Client의 정보, queryId, query등 다양한 정보를 확인 할 수 있습니다.
COMPLETED EVENT
{
"eventPayload": {
"metadata": {
"queryId": "20250617_133015_00113_w86kp",
"transactionId": "001b8117-00b5-48ee-ae27-01f318902c73",
"query": "SELECT\r\n u.user_id,\r\n order_id,\r\n date,\r\n items\r\nFROM mongodb.mydb.nestedcollection u\r\nCROSS JOIN UNNEST(u.orders) AS t(order_id, date, items)\nLIMIT 200",
"queryState": "FINISHED",
"tables": [
{
"catalog": "mongodb",
"schema": "mydb",
"table": "nestedcollection",
"authorization": "admin",
"filters": [],
"columns": [
{
"column": "user_id"
},
{
"column": "orders"
}
],
"directlyReferenced": true,
"referenceChain": []
}
],
"routines": [],
"uri": "http://******:8080/v1/query/20250617_133015_00113_w86kp",
"plan": "Trino version: 464\nQueued: 840.73us, Analysis: 1.47ms, Planning: 7.03ms, Execution: 40.70ms\nFragment 0 [SINGLE]\n CPU: 1.22ms, Scheduled: 1.24ms, Blocked 84.82ms (Input: 63.37ms, Output: 0.00ns), Input: 8 rows (639B); per task: avg.: 8.00 std.dev.: 0.00, Output: 8 rows (639B)\n Peak Memory: 0B, Tasks count: 1; per task: max: 1.10kB\n Output layout: [user_id, order_id, date, items]\n Output partitioning: SINGLE []\n Output[columnNames = [user_id, order_id, date, items]]\n │ Layout: [user_id:bigint, order_id:bigint, date:varchar, items:array(row(product varchar, qty bigint, price double))]\n │ Estimates: {rows: 200 (25kB), cpu: 0, memory: 0B, network: 0B}\n │ CPU: 0.00ns (0.00%), Scheduled: 0.00ns (0.00%), Blocked: 0.00ns (0.00%), Output: 8 rows (639B)\n │ Input avg.: 8.00 rows, Input std.dev.: 0.00%\n └─ Limit[count = 200]\n │ Layout: [user_id:bigint, order_id:bigint, date:varchar, items:array(row(product varchar, qty bigint, price double))]\n │ Estimates: {rows: 200 (25kB), cpu: 25k, memory: 0B, network: 0B}\n │ CPU: 0.00ns (0.00%), Scheduled: 0.00ns (0.00%), Blocked: 0.00ns (0.00%), Output: 8 rows (639B)\n │ Input avg.: 8.00 rows, Input std.dev.: 0.00%\n └─ LocalExchange[partitioning = SINGLE]\n │ Layout: [user_id:bigint, order_id:bigint, date:varchar, items:array(row(product varchar, qty bigint, price double))]\n │ Estimates: {rows: 200 (25kB), cpu: 0, memory: 0B, network: 0B}\n │ CPU: 0.00ns (0.00%), Scheduled: 0.00ns (0.00%), Blocked: 22.00ms (25.88%), Output: 8 rows (639B)\n │ Input avg.: 2.00 rows, Input std.dev.: 173.21%\n └─ RemoteSource[sourceFragmentIds = [1]]\n Layout: [user_id:bigint, order_id:bigint, date:varchar, items:array(row(product varchar, qty bigint, price double))]\n CPU: 0.00ns (0.00%), Scheduled: 0.00ns (0.00%), Blocked: 63.00ms (74.12%), Output: 8 rows (639B)\n Input avg.: 2.00 rows, Input std.dev.: 173.21%\n\nFragment 1 [SOURCE]\n CPU: 3.13ms, Scheduled: 4.74ms, Blocked 0.00ns (Input: 0.00ns, Output: 0.00ns), Input: 6 rows (659B); per task: avg.: 6.00 std.dev.: 0.00, Output: 8 rows (653B)\n Peak Memory: 0B, Tasks count: 1; per task: max: 0B\n Output layout: [user_id, order_id, date, items]\n Output partitioning: SINGLE []\n LimitPartial[count = 200]\n │ Layout: [user_id:bigint, order_id:bigint, date:varchar, items:array(row(product varchar, qty bigint, price double))]\n │ Estimates: {rows: 200 (25kB), cpu: 25k, memory: 0B, network: 0B}\n │ CPU: 0.00ns (0.00%), Scheduled: 0.00ns (0.00%), Blocked: 0.00ns (0.00%), Output: 8 rows (653B)\n │ Input avg.: 8.00 rows, Input std.dev.: 0.00%\n └─ CrossJoin Unnest[replicate = [user_id:bigint], unnest = [orders:array(row(order_id bigint, date varchar, items array(row(product varchar, qty bigint, price double))))]]\n │ Layout: [user_id:bigint, order_id:bigint, date:varchar, items:array(row(product varchar, qty bigint, price double))]\n │ CPU: 0.00ns (0.00%), Scheduled: 0.00ns (0.00%), Blocked: 0.00ns (0.00%), Output: 8 rows (653B)\n │ Input avg.: 6.00 rows, Input std.dev.: 0.00%\n └─ TableScan[table = mongodb:MongoTableHandle[schemaTableName=mydb.nestedcollection, remoteTableName=RemoteTableName[databaseName=mydb, collectionName=nestedcollection], filter=Optional.empty, constraint=ALL, projectedColumns=[user_id:bigint, orders:array(row(order_id bigint, date varchar, items array(row(product varchar, qty bigint, price double))))], limit=OptionalInt.empty]]\n Layout: [user_id:bigint, orders:array(row(order_id bigint, date varchar, items array(row(product varchar, qty bigint, price double))))]\n Estimates: {rows: ? (?), cpu: ?, memory: 0B, network: 0B}\n CPU: 3.00ms (100.00%), Scheduled: 4.00ms (100.00%), Blocked: 0.00ns (0.00%), Output: 6 rows (659B)\n Input avg.: 6.00 rows, Input std.dev.: 0.00%\n user_id := user_id:bigint\n orders := orders:array(row(order_id bigint, date varchar, items array(row(product varchar, qty bigint, price double))))\n Input: 6 rows (659B)\n\n",
"jsonPlan": "{\n \"0\" : {\n \"id\" : \"8\",\n \"name\" : \"Output\",\n \"descriptor\" : {\n \"columnNames\" : \"[user_id, order_id, date, items]\"\n },\n \"outputs\" : [ {\n \"type\" : \"bigint\",\n \"name\" : \"user_id\"\n }, {\n \"type\" : \"bigint\",\n \"name\" : \"order_id\"\n }, {\n \"type\" : \"varchar\",\n \"name\" : \"date\"\n }, {\n \"type\" : \"array(row(\\\"product\\\" varchar,\\\"qty\\\" bigint,\\\"price\\\" double))\",\n \"name\" : \"items\"\n } ],\n \"details\" : [ ],\n \"estimates\" : [ {\n \"outputRowCount\" : 200.0,\n \"outputSizeInBytes\" : 25600.0,\n \"cpuCost\" : 0.0,\n \"memoryCost\" : 0.0,\n \"networkCost\" : 0.0\n } ],\n \"children\" : [ {\n \"id\" : \"143\",\n \"name\" : \"Limit\",\n \"descriptor\" : {\n \"count\" : \"200\",\n \"withTies\" : \"\",\n \"inputPreSortedBy\" : \"[]\"\n },\n \"outputs\" : [ {\n \"type\" : \"bigint\",\n \"name\" : \"user_id\"\n }, {\n \"type\" : \"bigint\",\n \"name\" : \"order_id\"\n }, {\n \"type\" : \"varchar\",\n \"name\" : \"date\"\n }, {\n \"type\" : \"array(row(\\\"product\\\" varchar,\\\"qty\\\" bigint,\\\"price\\\" double))\",\n \"name\" : \"items\"\n } ],\n \"details\" : [ ],\n \"estimates\" : [ {\n \"outputRowCount\" : 200.0,\n \"outputSizeInBytes\" : 25600.0,\n \"cpuCost\" : 25600.0,\n \"memoryCost\" : 0.0,\n \"networkCost\" : 0.0\n } ],\n \"children\" : [ {\n \"id\" : \"243\",\n \"name\" : \"LocalExchange\",\n \"descriptor\" : {\n \"partitioning\" : \"SINGLE\",\n \"isReplicateNullsAndAny\" : \"\",\n \"hashColumn\" : \"[]\",\n \"arguments\" : \"[]\"\n },\n \"outputs\" : [ {\n \"type\" : \"bigint\",\n \"name\" : \"user_id\"\n }, {\n \"type\" : \"bigint\",\n \"name\" : \"order_id\"\n }, {\n \"type\" : \"varchar\",\n \"name\" : \"date\"\n }, {\n \"type\" : \"array(row(\\\"product\\\" varchar,\\\"qty\\\" bigint,\\\"price\\\" double))\",\n \"name\" : \"items\"\n } ],\n \"details\" : [ ],\n \"estimates\" : [ {\n \"outputRowCount\" : 200.0,\n \"outputSizeInBytes\" : 25600.0,\n \"cpuCost\" : 0.0,\n \"memoryCost\" : 0.0,\n \"networkCost\" : 0.0\n } ],\n \"children\" : [ {\n \"id\" : \"202\",\n \"name\" : \"RemoteSource\",\n \"descriptor\" : {\n \"sourceFragmentIds\" : \"[1]\"\n },\n \"outputs\" : [ {\n \"type\" : \"bigint\",\n \"name\" : \"user_id\"\n }, {\n \"type\" : \"bigint\",\n \"name\" : \"order_id\"\n }, {\n \"type\" : \"varchar\",\n \"name\" : \"date\"\n }, {\n \"type\" : \"array(row(\\\"product\\\" varchar,\\\"qty\\\" bigint,\\\"price\\\" double))\",\n \"name\" : \"items\"\n } ],\n \"details\" : [ ],\n \"estimates\" : [ ],\n \"children\" : [ ]\n } ]\n } ]\n } ]\n },\n \"1\" : {\n \"id\" : \"201\",\n \"name\" : \"LimitPartial\",\n \"descriptor\" : {\n \"count\" : \"200\",\n \"withTies\" : \"\",\n \"inputPreSortedBy\" : \"[]\"\n },\n \"outputs\" : [ {\n \"type\" : \"bigint\",\n \"name\" : \"user_id\"\n }, {\n \"type\" : \"bigint\",\n \"name\" : \"order_id\"\n }, {\n \"type\" : \"varchar\",\n \"name\" : \"date\"\n }, {\n \"type\" : \"array(row(\\\"product\\\" varchar,\\\"qty\\\" bigint,\\\"price\\\" double))\",\n \"name\" : \"items\"\n } ],\n \"details\" : [ ],\n \"estimates\" : [ {\n \"outputRowCount\" : 200.0,\n \"outputSizeInBytes\" : 25600.0,\n \"cpuCost\" : 25600.0,\n \"memoryCost\" : 0.0,\n \"networkCost\" : 0.0\n } ],\n \"children\" : [ {\n \"id\" : \"2\",\n \"name\" : \"CrossJoin Unnest\",\n \"descriptor\" : {\n \"replicate\" : \"[user_id:bigint]\",\n \"unnest\" : \"[orders:array(row(order_id bigint, date varchar, items array(row(product varchar, qty bigint, price double))))]\"\n },\n \"outputs\" : [ {\n \"type\" : \"bigint\",\n \"name\" : \"user_id\"\n }, {\n \"type\" : \"bigint\",\n \"name\" : \"order_id\"\n }, {\n \"type\" : \"varchar\",\n \"name\" : \"date\"\n }, {\n \"type\" : \"array(row(\\\"product\\\" varchar,\\\"qty\\\" bigint,\\\"price\\\" double))\",\n \"name\" : \"items\"\n } ],\n \"details\" : [ ],\n \"estimates\" : [ ],\n \"children\" : [ {\n \"id\" : \"0\",\n \"name\" : \"TableScan\",\n \"descriptor\" : {\n \"table\" : \"mongodb:MongoTableHandle[schemaTableName=mydb.nestedcollection, remoteTableName=RemoteTableName[databaseName=mydb, collectionName=nestedcollection], filter=Optional.empty, constraint=ALL, projectedColumns=[user_id:bigint, orders:array(row(order_id bigint, date varchar, items array(row(product varchar, qty bigint, price double))))], limit=OptionalInt.empty]\"\n },\n \"outputs\" : [ {\n \"type\" : \"bigint\",\n \"name\" : \"user_id\"\n }, {\n \"type\" : \"array(row(\\\"order_id\\\" bigint,\\\"date\\\" varchar,\\\"items\\\" array(row(\\\"product\\\" varchar,\\\"qty\\\" bigint,\\\"price\\\" double))))\",\n \"name\" : \"orders\"\n } ],\n \"details\" : [ \"user_id := user_id:bigint\", \"orders := orders:array(row(order_id bigint, date varchar, items array(row(product varchar, qty bigint, price double))))\" ],\n \"estimates\" : [ {\n \"outputRowCount\" : \"NaN\",\n \"outputSizeInBytes\" : \"NaN\",\n \"cpuCost\" : \"NaN\",\n \"memoryCost\" : 0.0,\n \"networkCost\" : 0.0\n } ],\n \"children\" : [ ]\n } ]\n } ]\n }\n}",
"payload": "{\"stageId\":\"20250617_133015_00113_w86kp.0\",\"state\":\"FINISHED\",\"plan\":{\"id\":\"0\",\"root\":{\"@type\":\"output\",\"id\":\"8\",\"source\":{\"@type\":\"limit\",\"id\":\"143\",\"source\":{\"@type\":\"exchange\",\"id\":\"243\",\"type\":\"GATHER\",\"scope\":\"LOCAL\",\"partitioningScheme\":{\"partitioning\":{\"handle\":{\"connectorHandle\":{\"@type\":\"system:io.trino.sql.planner.SystemPartitioningHandle\",\"partitioning\":\"SINGLE\",\"function\":\"SINGLE\"},\"scaleWriters\":false},\"arguments\":[]},\"outputLayout\":[{\"type\":\"bigint\",\"name\":\"user_id\"},{\"type\":\"bigint\",\"name\":\"order_id\"},{\"type\":\"varchar\",\"name\":\"date\"},{\"type\":\"array(row(\\\"product\\\" varchar,\\\"qty\\\" bigint,\\\"price\\\" double))\",\"name\":\"items\"}],\"replicateNullsAndAny\":false},\"sources\":[{\"@type\":\"remoteSource\",\"id\":\"202\",\"sourceFragmentIds\":[\"1\"],\"outputs\":[{\"type\":\"bigint\",\"name\":\"user_id\"},{\"type\":\"bigint\",\"name\":\"order_id\"},{\"type\":\"varchar\",\"name\":\"date\"},{\"type\":\"array(row(\\\"product\\\" varchar,\\\"qty\\\" bigint,\\\"price\\\" double))\",\"name\":\"items\"}],\"exchangeType\":\"GATHER\",\"retryPolicy\":\"NONE\"}],\"inputs\":[[{\"type\":\"bigint\",\"name\":\"user_id\"},{\"type\":\"bigint\",\"name\":\"order_id\"},{\"type\":\"varchar\",\"name\":\"date\"},{\"type\":\"array(row(\\\"product\\\" varchar,\\\"qty\\\" bigint,\\\"price\\\" double))\",\"name\":\"items\"}]]},\"count\":200,\"partial\":false,\"requiresPreSortedInputs\":[]},\"columns\":[\"user_id\",\"order_id\",\"date\",\"items\"],\"outputs\":[{\"type\":\"bigint\",\"name\":\"user_id\"},{\"type\":\"bigint\",\"name\":\"order_id\"},{\"type\":\"varchar\",\"name\":\"date\"},{\"type\":\"array(row(\\\"product\\\" varchar,\\\"qty\\\" bigint,\\\"price\\\" double))\",\"name\":\"items\"}]},\"symbols\":[{\"type\":\"bigint\",\"name\":\"user_id\"},{\"type\":\"bigint\",\"name\":\"order_id\"},{\"type\":\"varchar\",\"name\":\"date\"},{\"type\":\"array(row(\\\"product\\\" varchar,\\\"qty\\\" bigint,\\\"price\\\" double))\",\"name\":\"items\"}],\"partitioning\":{\"connectorHandle\":{\"@type\":\"system:io.trino.sql.planner.SystemPartitioningHandle\",\"partitioning\":\"SINGLE\",\"function\":\"SINGLE\"},\"scaleWriters\":false},\"partitionedSources\":[],\"outputPartitioningScheme\":{\"partitioning\":{\"handle\":{\"connectorHandle\":{\"@type\":\"system:io.trino.sql.planner.SystemPartitioningHandle\",\"partitioning\":\"SINGLE\",\"function\":\"SINGLE\"},\"scaleWriters\":false},\"arguments\":[]},\"outputLayout\":[{\"type\":\"bigint\",\"name\":\"user_id\"},{\"type\":\"bigint\",\"name\":\"order_id\"},{\"type\":\"varchar\",\"name\":\"date\"},{\"type\":\"array(row(\\\"product\\\" varchar,\\\"qty\\\" bigint,\\\"price\\\" 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varchar,\\\"qty\\\" bigint,\\\"price\\\" double))))\",\"name\":\"orders\"},\"outputs\":[{\"type\":\"bigint\",\"name\":\"order_id\"},{\"type\":\"varchar\",\"name\":\"date\"},{\"type\":\"array(row(\\\"product\\\" varchar,\\\"qty\\\" bigint,\\\"price\\\" double))\",\"name\":\"items\"}]}],\"joinType\":\"INNER\"},\"count\":200,\"partial\":true,\"requiresPreSortedInputs\":[]},\"symbols\":[{\"type\":\"bigint\",\"name\":\"user_id\"},{\"type\":\"bigint\",\"name\":\"order_id\"},{\"type\":\"varchar\",\"name\":\"date\"},{\"type\":\"array(row(\\\"product\\\" varchar,\\\"qty\\\" bigint,\\\"price\\\" double))\",\"name\":\"items\"},{\"type\":\"array(row(\\\"order_id\\\" bigint,\\\"date\\\" varchar,\\\"items\\\" array(row(\\\"product\\\" varchar,\\\"qty\\\" bigint,\\\"price\\\" 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\\\"name\\\" : \\\"LimitPartial\\\",\\n \\\"descriptor\\\" : {\\n \\\"count\\\" : \\\"200\\\",\\n \\\"withTies\\\" : \\\"\\\",\\n \\\"inputPreSortedBy\\\" : \\\"[]\\\"\\n },\\n \\\"outputs\\\" : [ {\\n \\\"type\\\" : \\\"bigint\\\",\\n \\\"name\\\" : \\\"user_id\\\"\\n }, {\\n \\\"type\\\" : \\\"bigint\\\",\\n \\\"name\\\" : \\\"order_id\\\"\\n }, {\\n \\\"type\\\" : \\\"varchar\\\",\\n \\\"name\\\" : \\\"date\\\"\\n }, {\\n \\\"type\\\" : \\\"array(row(\\\\\\\"product\\\\\\\" varchar,\\\\\\\"qty\\\\\\\" bigint,\\\\\\\"price\\\\\\\" double))\\\",\\n \\\"name\\\" : \\\"items\\\"\\n } ],\\n \\\"details\\\" : [ ],\\n \\\"estimates\\\" : [ ],\\n \\\"children\\\" : [ {\\n \\\"id\\\" : \\\"2\\\",\\n \\\"name\\\" : \\\"CrossJoin Unnest\\\",\\n \\\"descriptor\\\" : {\\n \\\"replicate\\\" : \\\"[user_id:bigint]\\\",\\n \\\"unnest\\\" : \\\"[orders:array(row(order_id bigint, date varchar, items array(row(product varchar, qty bigint, price double))))]\\\"\\n },\\n \\\"outputs\\\" : [ {\\n \\\"type\\\" : \\\"bigint\\\",\\n \\\"name\\\" : \\\"user_id\\\"\\n }, {\\n \\\"type\\\" : \\\"bigint\\\",\\n \\\"name\\\" : \\\"order_id\\\"\\n }, {\\n \\\"type\\\" : \\\"varchar\\\",\\n \\\"name\\\" : \\\"date\\\"\\n }, {\\n \\\"type\\\" : \\\"array(row(\\\\\\\"product\\\\\\\" varchar,\\\\\\\"qty\\\\\\\" bigint,\\\\\\\"price\\\\\\\" double))\\\",\\n \\\"name\\\" : \\\"items\\\"\\n } ],\\n \\\"details\\\" : [ ],\\n \\\"estimates\\\" : [ ],\\n \\\"children\\\" : [ {\\n \\\"id\\\" : \\\"0\\\",\\n \\\"name\\\" : \\\"TableScan\\\",\\n \\\"descriptor\\\" : {\\n \\\"table\\\" : \\\"mongodb:MongoTableHandle[schemaTableName=mydb.nestedcollection, remoteTableName=RemoteTableName[databaseName=mydb, collectionName=nestedcollection], filter=Optional.empty, constraint=ALL, projectedColumns=[user_id:bigint, orders:array(row(order_id bigint, date varchar, items array(row(product varchar, qty bigint, price double))))], limit=OptionalInt.empty]\\\"\\n },\\n \\\"outputs\\\" : [ {\\n \\\"type\\\" : \\\"bigint\\\",\\n \\\"name\\\" : \\\"user_id\\\"\\n }, {\\n \\\"type\\\" : \\\"array(row(\\\\\\\"order_id\\\\\\\" bigint,\\\\\\\"date\\\\\\\" varchar,\\\\\\\"items\\\\\\\" array(row(\\\\\\\"product\\\\\\\" varchar,\\\\\\\"qty\\\\\\\" bigint,\\\\\\\"price\\\\\\\" double))))\\\",\\n \\\"name\\\" : \\\"orders\\\"\\n } ],\\n \\\"details\\\" : [ \\\"user_id := user_id:bigint\\\", \\\"orders := orders:array(row(order_id bigint, date varchar, items array(row(product varchar, qty bigint, price double))))\\\" ],\\n \\\"estimates\\\" : [ ],\\n \\\"children\\\" : [ ]\\n } ]\\n } ]\\n}\"},\"coordinatorOnly\":false,\"types\":[\"bigint\",\"bigint\",\"varchar\",\"array(row(\\\"product\\\" varchar,\\\"qty\\\" bigint,\\\"price\\\" 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(s)\":{\"@class\":\"io.trino.plugin.base.metrics.TDigestHistogram\",\"digest\":\"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAABZQAAAAAAAAPA/AQAAAAAAAAAAAAAAAAAAAAAA8D8=\",\"min\":0.0,\"max\":0.0,\"p25\":0.0,\"p50\":0.0,\"p75\":0.0,\"p90\":0.0,\"p95\":0.0,\"p99\":0.0,\"total\":1,\"p01\":0.0,\"p05\":0.0,\"p10\":0.0}},\"connectorMetrics\":{},\"pipelineMetrics\":{},\"physicalWrittenDataSize\":\"0B\",\"blockedWall\":\"0.00ns\",\"finishCalls\":1,\"finishWall\":\"1.09us\",\"finishCpu\":\"1.12us\",\"userMemoryReservation\":\"0B\",\"revocableMemoryReservation\":\"0B\",\"peakUserMemoryReservation\":\"0B\",\"peakRevocableMemoryReservation\":\"0B\",\"peakTotalMemoryReservation\":\"0B\",\"spilledDataSize\":\"0B\"},{\"stageId\":1,\"pipelineId\":0,\"operatorId\":3,\"planNodeId\":\"201\",\"operatorType\":\"TaskOutputOperator\",\"totalDrivers\":1,\"addInputCalls\":1,\"addInputWall\":\"281.89us\",\"addInputCpu\":\"281.42us\",\"physicalInputDataSize\":\"0B\",\"physicalInputPositions\":0,\"physicalInputReadTime\":\"0.00ns\",\"internalNetworkInputDataSize\":\"0B\",\"internalNetworkInputPositions\":0,\"rawInputDataSize\":\"0B\",\"inputDataSize\":\"653B\",\"inputPositions\":8,\"sumSquaredInputPositions\":64.0,\"getOutputCalls\":0,\"getOutputWall\":\"0.00ns\",\"getOutputCpu\":\"0.00ns\",\"outputDataSize\":\"653B\",\"outputPositions\":8,\"dynamicFilterSplitsProcessed\":0,\"metrics\":{\"CPU 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(s)\":{\"@class\":\"io.trino.plugin.base.metrics.TDigestHistogram\",\"digest\":\"ABTcxqGplzI/FNzGoamXMj8AAAAAAABZQAAAAAAAAPA/AQAAABTcxqGplzI/AAAAAAAA8D8=\",\"min\":2.83698E-4,\"max\":2.83698E-4,\"p25\":2.83698E-4,\"p50\":2.83698E-4,\"p75\":2.83698E-4,\"p90\":2.83698E-4,\"p95\":2.83698E-4,\"p99\":2.83698E-4,\"total\":1,\"p01\":2.83698E-4,\"p05\":2.83698E-4,\"p10\":2.83698E-4},\"Blocked time distribution (s)\":{\"@class\":\"io.trino.plugin.base.metrics.TDigestHistogram\",\"digest\":\"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAABZQAAAAAAAAPA/AQAAAAAAAAAAAAAAAAAAAAAA8D8=\",\"min\":0.0,\"max\":0.0,\"p25\":0.0,\"p50\":0.0,\"p75\":0.0,\"p90\":0.0,\"p95\":0.0,\"p99\":0.0,\"total\":1,\"p01\":0.0,\"p05\":0.0,\"p10\":0.0}},\"connectorMetrics\":{},\"pipelineMetrics\":{},\"physicalWrittenDataSize\":\"0B\",\"blockedWall\":\"0.00ns\",\"finishCalls\":1,\"finishWall\":\"1.81us\",\"finishCpu\":\"1.84us\",\"userMemoryReservation\":\"0B\",\"revocableMemoryReservation\":\"0B\",\"peakUserMemoryReservation\":\"64B\",\"peakRevocableMemoryReservation\":\"0B\",\"peakTotalMemoryReservation\":\"64B\",\"spilledDataSize\":\"0B\"}],\"drivers\":[]}]},\"needsPlan\":false}],\"subStages\":[],\"tables\":{\"0\":{\"connectorName\":\"mongodb\",\"tableName\":\"mongodb.mydb.nestedcollection\",\"predicate\":{\"columnDomains\":[]}}}}],\"tables\":{}}"
},
"statistics": {
"cpuTime": 0.004000000,
"failedCpuTime": 0.0,
"wallTime": 0.043000000,
"queuedTime": 0.001000000,
"scheduledTime": 0.006000000,
"failedScheduledTime": 0.0,
"resourceWaitingTime": 0.002000000,
"analysisTime": 0.001000000,
"planningTime": 0.007000000,
"planningCpuTime": 0.007000000,
... 생략 ...
"ioMetadata": {
"inputs": [
{
"catalogName": "mongodb",
"catalogVersion": "default",
"schema": "mydb",
"table": "nestedcollection",
"columns": [
"user_id",
"orders"
],
"connectorMetrics": {},
"physicalInputBytes": 0,
"physicalInputRows": 6
}
]
},
"warnings": [],
"createTime": "2025-06-17T13:30:15.809Z",
"executionStartTime": "2025-06-17T13:30:15.811Z",
"endTime": "2025-06-17T13:30:15.852Z"
},
"eventMetadata": {}
}create event 토픽에 저장된 데이터보다 훨씬 많은 정보를 담고 있습니다.
쿼리의 상태를 나타내는 queryState와 관련한 통계정보들도 볼 수 있습니다.
Kafka에 들어오는 쿼리를 분석하여 실시간으로 Trino의 들어오는 쿼리를 분석해볼수도 있을 것 같습니다.
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