Flink’s Table API and SQL support are unified APIs for batch and stream processing. This means that Table API and SQL queries have the same semantics regardless whether their input is bounded batch input or unbounded stream input. Because the relational algebra and SQL were originally designed for batch processing, relational queries on unbounded streaming input are not as well understood as relational queries on bounded batch input.
On this page, we explain concepts, practical limitations, and stream-specific configuration parameters of Flink’s relational APIs on streaming data.
SQL and the relational algebra have not been designed with streaming data in mind. As a consequence, there are few conceptual gaps between relational algebra (and SQL) and stream processing.
|Relational Algebra / SQL||Stream Processing|
|Relations (or tables) are bounded (multi-)sets of tuples.||A stream is an infinite sequences of tuples.|
|A query that is executed on batch data (e.g., a table in a relational database) has access to the complete input data.||A streaming query cannot access all data when is started and has to "wait" for data to be streamed in.|
|A batch query terminates after it produced a fixed sized result.||A streaming query continuously updates its result based on the received records and never completes.|
Despite these differences, processing streams with relational queries and SQL is not impossible. Advanced relational database systems offer a feature called Materialized Views. A materialized view is defined as a SQL query, just like a regular virtual view. In contrast to a virtual view, a materialized view caches the result of the query such that the query does not need to be evaluated when the view is accessed. A common challenge for caching is to prevent a cache from serving outdated results. A materialized view becomes outdated when the base tables of its definition query are modified. Eager View Maintenance is a technique to update materialized views and updates a materialized view as soon as its base tables are updated.
The connection between eager view maintenance and SQL queries on streams becomes obvious if we consider the following:
DELETEDML statements, often called changelog stream.
With these points in mind, we introduce Flink’s concept of Dynamic Tables in the next section.
Dynamic tables are the core concept of Flink’s Table API and SQL support for streaming data. In contrast to the static tables that represent batch data, dynamic table are changing over time. They can be queried like static batch tables. Querying a dynamic table yields a Continuous Query. A continuous query never terminates and produces a dynamic table as result. The query continuously updates its (dynamic) result table to reflect the changes on its input (dynamic) table. Essentially, a continuous query on a dynamic table is very similar to the definition query of a materialized view.
It is important to note that the result of a continuous query is always semantically equivalent to the result of the same query being executed in batch mode on a snapshot of the input tables.
The following figure visualizes the relationship of streams, dynamic tables, and continuous queries:
Note: Dynamic tables are foremost a logical concept. Dynamic tables are not necessarily (fully) materialized during query execution.
In the following, we will explain the concepts of dynamic tables and continuous queries with a stream of click events that have the following schema:
[ user: VARCHAR, // the name of the user cTime: TIMESTAMP, // the time when the URL was accessed url: VARCHAR // the URL that was accessed by the user ]
In order to process a stream with a relational query, it has to be converted into a
Table. Conceptually, each record of the stream is interpreted as an
INSERT modification on the resulting table. Essentially, we are building a table from an
INSERT-only changelog stream.
The following figure visualizes how the stream of click event (left-hand side) is converted into a table (right-hand side). The resulting table is continuously growing as more records of the click stream are inserted.
Note: A table which is defined on a stream is internally not materialized.
A continuous query is evaluated on a dynamic table and produces a new dynamic table as result. In contrast to a batch query, a continuous query never terminates and updates its result table according to the updates on its input tables. At any point in time, the result of a continuous query is semantically equivalent to the result of the same query being executed in batch mode on a snapshot of the input tables.
In the following we show two example queries on a
clicks table that is defined on the stream of click events.
The first query is a simple
GROUP-BY COUNT aggregation query. It groups the
clicks table on the
user field and counts the number of visited URLs. The following figure shows how the query is evaluated over time as the
clicks table is updated with additional rows.
When the query is started, the
clicks table (left-hand side) is empty. The query starts to compute the result table, when the first row is inserted into the
clicks table. After the first row
[Mary, ./home] was inserted, the result table (right-hand side, top) consists of a single row
[Mary, 1]. When the second row
[Bob, ./cart] is inserted into the
clicks table, the query updates the result table and inserts a new row
[Bob, 1]. The third row
[Mary, ./prod?id=1] yields an update of an already computed result row such that
[Mary, 1] is updated to
[Mary, 2]. Finally, the query inserts a third row
[Liz, 1] into the result table, when the fourth row is appended to the
The second query is similar to the first one but groups the
clicks table in addition to the
user attribute also on an hourly tumbling window before it counts the number of URLs (time-based computations such as windows are based on special time attributes, which are discussed below.). Again, the figure shows the input and output at different points in time to visualize the changing nature of dynamic tables.
As before, the input table
clicks is shown on the left. The query continuously computes results every hour and updates the result table. The clicks table contains four rows with timestamps (
12:59:59. The query computes two results rows from this input (one for each
user) and appends them to the result table. For the next window between
clicks table contains three rows, which results in another two rows being appended to the result table. The result table is updated, as more rows are appended to
clicks over time.
Although the two example queries appear to be quite similar (both compute a grouped count aggregate), they differ in one important aspect:
Whether a query produces an append-only table or an updated table has some implications:
Many, but not all, semantically valid queries can be evaluated as continuous queries on streams. Some queries are too expensive to compute, either due to the size of state that they need to maintain or because computing updates is too expensive.
RANKbased on the time of the last click. As soon as the
clickstable receives a new row, the
lastActionof the user is updated and a new rank must be computed. However since two rows cannot have the same rank, all lower ranked rows need to be updated as well.
The QueryConfig section discusses parameters to control the execution of continuous queries. Some parameters can be used to trade the size of maintained state for result accuracy.
A dynamic table can be continuously modified by
DELETE changes just like a regular database table. It might be a table with a single row, which is constantly updated, an insert-only table without
DELETE modifications, or anything in between.
When converting a dynamic table into a stream or writing it to an external system, these changes need to be encoded. Flink’s Table API and SQL support three ways to encode the changes of a dynamic table:
Append-only stream: A dynamic table that is only modified by
INSERT changes can be converted into a stream by emitting the inserted rows.
Retract stream: A retract stream is a stream with two types of messages, add messages and retract messages. A dynamic table is converted into an retract stream by encoding an
INSERT change as add message, a
DELETE change as retract message, and an
UPDATE change as a retract message for the updated (previous) row and an add message for the updating (new) row. The following figure visualizes the conversion of a dynamic table into a retract stream.
UPDATEchanges as upsert message and
DELETEchanges as delete message. The stream consuming operator needs to be aware of the unique key attribute in order to apply messages correctly. The main difference to a retract stream is that
UPDATEchanges are encoded with a single message and hence more efficient. The following figure visualizes the conversion of a dynamic table into an upsert stream.
The API to convert a dynamic table into a
DataStream is discussed on the Common Concepts page. Please note that only append and retract streams are supported when converting a dynamic table into a
TableSink interface to emit a dynamic table to an external system are discussed on the TableSources and TableSinks page.
Flink is able to process streaming data based on different notions of time.
For more information about time handling in Flink, see the introduction about Event Time and Watermarks.
Table programs require that the corresponding time characteristic has been specified for the streaming environment:
Time-based operations such as windows in both the Table API and SQL require information about the notion of time and its origin. Therefore, tables can offer logical time attributes for indicating time and accessing corresponding timestamps in table programs.
Time attributes can be part of every table schema. They are defined when creating a table from a
DataStream or are pre-defined when using a
TableSource. Once a time attribute has been defined at the beginning, it can be referenced as a field and can used in time-based operations.
As long as a time attribute is not modified and is simply forwarded from one part of the query to another, it remains a valid time attribute. Time attributes behave like regular timestamps and can be accessed for calculations. If a time attribute is used in a calculation, it will be materialized and becomes a regular timestamp. Regular timestamps do not cooperate with Flink’s time and watermarking system and thus can not be used for time-based operations anymore.
Processing time allows a table program to produce results based on the time of the local machine. It is the simplest notion of time but does not provide determinism. It neither requires timestamp extraction nor watermark generation.
There are two ways to define a processing time attribute.
The processing time attribute is defined with the
.proctime property during schema definition. The time attribute must only extend the physical schema by an additional logical field. Thus, it can only be defined at the end of the schema definition.
The processing time attribute is defined by a
TableSource that implements the
DefinedProctimeAttribute interface. The logical time attribute is appended to the physical schema defined by the return type of the
Event time allows a table program to produce results based on the time that is contained in every record. This allows for consistent results even in case of out-of-order events or late events. It also ensures replayable results of the table program when reading records from persistent storage.
Additionally, event time allows for unified syntax for table programs in both batch and streaming environments. A time attribute in a streaming environment can be a regular field of a record in a batch environment.
In order to handle out-of-order events and distinguish between on-time and late events in streaming, Flink needs to extract timestamps from events and make some kind of progress in time (so-called watermarks).
An event time attribute can be defined either during DataStream-to-Table conversion or by using a TableSource.
The event time attribute is defined with the
.rowtime property during schema definition. Timestamps and watermarks must have been assigned in the
DataStream that is converted.
There are two ways of defining the time attribute when converting a
DataStream into a
Table. Depending on whether the specified
.rowtime field name exists in the schema of the
DataStream or not, the timestamp field is either
In either case the event time timestamp field will hold the value of the
DataStream event time timestamp.
The event time attribute is defined by a
TableSource that implements the
DefinedRowtimeAttribute interface. The
getRowtimeAttribute() method returns the name of an existing field that carries the event time attribute of the table and is of type
DataStream returned by the
getDataStream() method must have watermarks assigned that are aligned with the defined time attribute. Please note that the timestamps of the
DataStream (the ones which are assigned by a
TimestampAssigner) are ignored. Only the values of the
TableSource’s rowtime attribute are relevant.
Table API and SQL queries have the same semantics regardless whether their input is bounded batch input or unbounded stream input. In many cases, continuous queries on streaming input are capable of computing accurate results that are identical to offline computed results. However, this is not possible in general case because continuous queries have to restrict the size of the state they are maintaining in order to avoid to run out of storage and to be able to process unbounded streaming data over a long period of time. As a result, a continuous query might only be able to provide approximated results depending on the characteristics of the input data and the query itself.
Flink’s Table API and SQL interface provide parameters to tune the accuracy and resource consumption of continuous queries. The parameters are specified via a
QueryConfig object. The
QueryConfig can be obtained from the
TableEnvironment and is passed back when a
Table is translated, i.e., when it is transformed into a DataStream or emitted via a TableSink.
In the following we describe the parameters of the
QueryConfig and how they affect the accuracy and resource consumption of a query.
Many queries aggregate or join records on one or more key attributes. When such a query is executed on a stream, the continuous query needs to collect records or maintain partial results per key. If the key domain of the input stream is evolving, i.e., the active key values are changing over time, the continuous query accumulates more and more state as more and more distinct keys are observed. However, often keys become inactive after some time and their corresponding state becomes stale and useless.
For example the following query computes the number of clicks per session.
SELECT sessionId, COUNT(*) FROM clicks GROUP BY sessionId;
sessionId attribute is used as a grouping key and the continuous query maintains a count for each
sessionId it observes. The
sessionId attribute is evolving over time and
sessionId values are only active until the session ends, i.e., for a limited period of time. However, the continuous query cannot know about this property of
sessionId and expects that every
sessionId value can occur at any point of time. It maintains a count for each observed
sessionId value. Consequently, the total state size of the query is continuously growing as more and more
sessionId values are observed.
The Idle State Retention Time parameters define for how long the state of a key is retained without being updated before it is removed. For the previous example query, the count of a
sessionId would be removed as soon as it has not been updated for the configured period of time.
By removing the state of a key, the continuous query completely forgets that it has seen this key before. If a record with a key, whose state has been removed before, is processed, the record will be treated as if it was the first record with the respective key. For the example above this means that the count of a
sessionId would start again at
There are two parameters to configure the idle state retention time:
The parameters are specified as follows:
Configuring different minimum and maximum idle state retention times is more efficient because it reduces the internal book-keeping of a query for when to remove state.