Stream Ingestion with Upsert
Upsert support in Apache Pinot.
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.
To update a record, a primary key is needed to uniquely identify the record. To define a primary key, add the field
primaryKeyColumnsto the schema definition. For example, the schema definition of
UpsertMeetupRSVPin the quick start example has this definition.
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.
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
sendAPI. 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.
There are a few configurations needed in the table configurations to enable upsert.
For append-only tables, the upsert mode defaults to
NONE. To enable the full upsert, set the
FULLfor the full update. For example:
upsert mode: full
Pinot also added the partial update support in v0.8.0+. To enable the partial upsert, set the
partialUpsertStrategiesfor partial upsert columns. Since v0.10.0,
defaultPartialUpsertStrategyis introduced as the default merge strategy for columns without specified strategy. For example:
upsert mode: partial (v0.8.0)
upsert mode: partial (v0.10.0+)
Pinot supports the following partial upsert strategies -
Note: If you don't specify any strategy for a given column, by default the value will always be overwritten by the new value for that column. In v0.10.0+, we added support for defaultPartialUpsertStrategy. The default value of defaultPartialUpsertStrategy is OVERWRITE.
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
comparisonColumnto override the column used for comparison. For example,
For partial upsert table, the out-of-order events won't be consumed and indexed. For example, for two records with the same primary key, if the record with the smaller value of the comparison column came later than the other record, it will be skipped.
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
strictReplicaGroupas the routing strategy. To use that, configure
Routingas the following:
There are some limitations for the upsert Pinot tables.
First, the high-level consumer is not allowed for the input stream ingestion, which means
Second, the star-tree index cannot be used for indexing, as the star-tree index performs pre-aggregation during the ingestion.
Third, unlike append-only tables, out-of-order events won't be consumed and indexed by Pinot partial upsert table, these late events will be skipped.
Unlike other real-time tables, Upsert table takes up more memory resources as it needs to bookkeep the record locations in memory. As a result, it's important to plan the capacity beforehand, and monitor the resource usage. Here are some recommended practices of using Upsert table.
- Create the Kafka topic with more partitions. The number of Kafka partitions determines the partition numbers of the Pinot table. The more partitions you have in the Kafka topic, more Pinot servers you can distribute the Pinot table to and therefore more you can scale the table horizontally.
- Upsert table maintains an in-memory map from the primary key to the record location. So it's recommended to use a simple primary key type and avoid composite primary keys to save the memory cost. In addition, consider the
hashFunctionconfig in the Upsert config, which can be
MURMUR3, to store the 128-bit hashcode of the primary key instead. This is useful when your primary key takes more space. But keep in mind, this hash may introduce collisions, though the chance is very low.
- Set up a dashboard over the metric
pinot.server.upsertPrimaryKeysCount.tableNameto watch the number of primary keys in a table partition. It's useful for tracking its growth which is proportional to the memory usage growth.
- Capacity planning. It's useful to plan the capacity beforehand to ensure you will not run into resource constraints later. A simple way is to measure the amount of the primary keys in the Kafka throughput per partition and time the primary key space cost to approximate the memory usage. A heap dump is also useful to check the memory usage so far on an upsert table instance.
Putting these together, you can find the table configurations of the quick start example as the following:
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
# stop previous quick start cluster, if any
You can also run partial upsert demo with the following command
# stop previous quick start cluster, if any
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
skipUpsertto skip the upsert effect in the query result.
Disable the upsert during query via query option