Stream Ingestion with Upsert

Pinot provides native support of upsert during the real-time ingestion (v0.6.0+). There are scenarios that the records need modifications, such as correcting a ride fare and updating a delivery status.
With the foundation of full upsert support in Pinot, another category of use cases on partial upsert are enabled (v0.8.0+). Partial upsert is convenient to users so that they only need to specify the columns whose value changes, and ignore the others.
To enable upsert on a Pinot table, there are a couple of configurations to make on the table configurations as well as on the input stream.
Define the primary key in the schema
To update a record, a primary key is needed to uniquely identify the record. To define a primary key, add the field primaryKeyColumns to the schema definition. For example, the schema definition of UpsertMeetupRSVP in the quick start example has this definition.
upsert_meetupRsvp_schema.json
1
{
2
    "primaryKeyColumns": ["event_id"]
3
}
Copied!
Note this field expects a list of columns, as the primary key can be composite.
When two records of the same primary key are ingested, the record with the greater event time (as defined by the time column) is used. When records with the same primary key and event time, then the order is not determined. In most cases, the later ingested record will be used, but may not be so in the cases when the table has a column to sort by.
Partition the input stream by the primary key
An important requirement for the Pinot upsert table is to partition the input stream by the primary key. For Kafka messages, this means the producer shall set the key in the send API. If the original stream is not partitioned, then a streaming processing job (e.g. Flink) is needed to shuffle and repartition the input stream into a partitioned one for Pinot's ingestion.
Enable upsert in the table configurations
There are a few configurations needed in the table configurations to enable upsert.
Upsert mode
For append-only tables, the upsert mode defaults to NONE. To enable the full upsert, set the mode to FULL for the full update. For example:
upsert mode: full
1
{
2
  "upsertConfig": {
3
    "mode": "FULL"
4
  }
5
}
Copied!
Pinot also added the partial update support in v0.8.0+. To enable the partial upsert, set the mode to PARTIAL and specify partialUpsertStrategies for partial upsert columns. For example:
upsert mode: partial
1
{
2
  "upsertConfig": {
3
    "mode": "PARTIAL",
4
    "partialUpsertStrategies":{
5
      "rsvp_count": "INCREMENT",
6
      "group_name": "UNION",
7
      "venue_name": "APPEND"
8
    }
9
  }
10
}
Copied!
Pinot supports the following partial upsert strategies -
Strategy
Description
OVERWRITE
Overwrite the column of the last record
INCREMENT
Add the new value to the existing values
APPEND
Add the new item to the Pinot unordered set
UNION
Add the new item to the Pinot unordered set if not exists
Comparison Column
By default, Pinot uses the value in the time column to determine the latest record. That means, for two records with the same primary key, the record with the larger value of the time column is picked as the latest update. However, there are cases when users need to use another column to determine the order. In such case, you can use option comparisonColumn to override the column used for comparison. For example,
comparison column
1
​
Copied!
1
{
2
  "upsertConfig": {
3
    "mode": "FULL",
4
    "comparisonColumn": "anotherTimeColumn"
5
  }
6
}
Copied!
Use strictReplicaGroup for routing
The upsert Pinot table can use only the low-level consumer for the input streams. As a result, it uses the partitioned replica-group assignment for the segments. Moreover,upsert poses the additional requirement that all segments of the same partition must be served from the same server to ensure the data consistency across the segments. Accordingly, it requires to use strictReplicaGroup as the routing strategy. To use that, configure instanceSelectorType in Routing as the following:
routing
1
{
2
  "routing": {
3
    "instanceSelectorType": "strictReplicaGroup"
4
  }
5
}
Copied!
Limitations
There are some limitations for the upsert Pinot tables.
First, the high-level consumer is not allowed for the input stream ingestion, which means stream.kafka.consumer.type must be lowLevel.
Second, the star-tree index cannot be used for indexing, as the star-tree index performs pre-aggregation during the ingestion.
Example
Putting these together, you can find the table configurations of the quick start example as the following:
upsert_meetupRsvp_realtime_table_config.json
1
{
2
  "tableName": "meetupRsvp",
3
  "tableType": "REALTIME",
4
  "segmentsConfig": {
5
    "timeColumnName": "mtime",
6
    "timeType": "MILLISECONDS",
7
    "retentionTimeUnit": "DAYS",
8
    "retentionTimeValue": "1",
9
    "segmentPushType": "APPEND",
10
    "segmentAssignmentStrategy": "BalanceNumSegmentAssignmentStrategy",
11
    "schemaName": "meetupRsvp",
12
    "replicasPerPartition": "1"
13
  },
14
  "tenants": {},
15
  "tableIndexConfig": {
16
    "loadMode": "MMAP",
17
    "streamConfigs": {
18
      "streamType": "kafka",
19
      "stream.kafka.consumer.type": "lowLevel",
20
      "stream.kafka.topic.name": "meetupRSVPEvents",
21
      "stream.kafka.decoder.class.name": "org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder",
22
      "stream.kafka.hlc.zk.connect.string": "localhost:2191/kafka",
23
      "stream.kafka.consumer.factory.class.name": "org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory",
24
      "stream.kafka.zk.broker.url": "localhost:2191/kafka",
25
      "stream.kafka.broker.list": "localhost:19092",
26
      "realtime.segment.flush.threshold.size": 30,
27
      "realtime.segment.flush.threshold.rows": 30
28
    }
29
  },
30
  "metadata": {
31
    "customConfigs": {}
32
  },
33
  "routing": {
34
    "instanceSelectorType": "strictReplicaGroup"
35
  },
36
  "upsertConfig": {
37
    "mode": "FULL"
38
  }
39
}
Copied!
Quick Start
To illustrate how the full upsert works, the Pinot binary comes with a quick start example. Use the following command to creates a realtime upsert table meetupRSVP.
1
# stop previous quick start cluster, if any
2
bin/quick-start-upsert-streaming.sh
Copied!
You can also run partial upsert demo with the following command
1
# stop previous quick start cluster, if any
2
bin/quick-start-partial-upsert-streaming.sh
Copied!
As soon as data flows into the stream, the Pinot table will consume it and it will be ready for querying. Head over to the Query Console to checkout the realtime data.
Query the upsert table
For partial upsert you can see only the value from configured column changed based on specified partial upsert strategy.
Query the partial upsert table
An example for partial upsert is shown below, each of the event_id kept being unique during ingestion, meanwhile the value of rsvp_count incremented.
Explain partial upsert table
To see the difference from the append-only table, you can use a query option skipUpsert to skip the upsert effect in the query result.
Disable the upsert during query via query option

Amazon Kinesis

File systems

Last modified 16d ago

Copy link

Contents

Define the primary key in the schema

Partition the input stream by the primary key

Enable upsert in the table configurations

Upsert mode

Comparison Column

Use strictReplicaGroup for routing

Limitations

Example

Quick Start