...
In the whole process of streaming data processing, there are mainly the following 4 How 5 Hows problems(general streaming and batch ETL):
...
Multiple ETL jobs and tables in Table Store form a topology. For example, there are dependencies between Table1, Table2, and Table3. Currently, users can't get relationship information between these tables, and when data on a table is late, it is difficult to trace the root cause based on job dependencies.
- HOW to
...
- share state between multiple jobs to save resources?
Multiple ETL jobs may have the same state data in different computation. For example, there're two JOIN ETL between three tables as follows
draw.io Diagram | |||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
HOW to define E2E data processing delay of ETL jobs and tables in topology above?
Flink ETL jobs update tables above in real-time, there are dependencies between them. While the data is flowing, how to define the data delay in these tables? For the above example, how to define the E2E delay of streaming data from CDC to Table2? How much does the delay of each ETL job affect the E2E delay, and which ETL job needs to be optimized?
HOW to revise the data in tables updated by streaming job?
When one of the tables needs to be revised, how to revise it in the streaming process on the premise of ensuring the correctness of the data? For instance, the data in Table1 needs to be revised, what should the users do in the topology to ensure that the data is not lost or repeated?
In order to answer the above questions, we introduce Timestamp Barrier
in Flink to align data, introduce MetaService in Table Store to coordinate Flink ETL jobs, manage the relationships and dependencies between ETL jobs and tables, and support data consistency in Table Store.
Proposed Design
Architecture
...
|
The data in State1
and Table1
are same, State2
, State3
and Table2
are same, State3
and Table3
are same.The more times the same table is joined, the more serious the resource waste is. We should reuse the data in the state and save resources.
HOW to define the data correctness in query?
draw.io Diagram | ||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
As shown above, Flink ETL jobs will generate V11, V21, V31 in Table1,2,3 respectively for V1 in CDC. Suppose the following case: there's a base table in database, a user creates cascaded views or materialized views Table1, Table2 and Table3 based on it. Then the user executes complex queries on Table1, Table2, and Table3 and the query results are consistent with the base table. When the user creates the three tables in Table Store and incrementally updates data of them by Flink ETL jobs in real-time, these tables can be regarded as materialized views that are incrementally updated in real time. In the process of streaming data processing, how to define data correctness in query when users perform join query on these three tables? The case in point is how to ensure V11, V21 and V31 are read or not read in one query?
HOW to define E2E data processing delay of ETL jobs and tables in topology above?
Flink ETL jobs update tables above in real-time, there are dependencies between them. While the data is flowing, how to define the data delay in these tables? For the above example, how to define the E2E delay of streaming data from CDC to Table2? How much does the delay of each ETL job affect the E2E delay, and which ETL job needs to be optimized?
HOW to revise the data in tables updated by streaming job?
When one of the tables needs to be revised, how to revise it in the streaming process on the premise of ensuring the correctness of the data? For instance, the data in Table1 needs to be revised, what should the users do in the topology to ensure that the data is not lost or repeated?
In order to answer the above questions, we introduce Timestamp Barrier
in Flink to align data, introduce MetaService in Table Store to coordinate Flink ETL jobs, manage the relationships and dependencies between ETL jobs and tables, and support data consistency in Table Store.
Proposed Design
Architecture
We can regard each Flink ETL job as a single node with complex computation, and the table in Table Store as a data stream. Flink ETL and tables form a huge streaming job, which we call ETL Topology
. We setup a MetaService node to manage the ETL Topology
. The main architecture is:
draw.io Diagram | ||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
There are two core points in the architecture: Timestamp Barrier Mechanism
and MetaService
Timestamp Barrier Mechanism
We need a barrier mechanism in Flink to guarantee the data consistency.
- Each ETL source needs to be assigned a unified
Timestamp Barrier
- Stateful and temporal operators in Flink ETL job align and compute data according to the barrier.
- Sink operator in ETL job confirms data with barrier to sink tables in Table Store.
MetaService
component
MetaService
is the coordinator in ETL Topology
, its capabilities are as followed:
1> Coordinate the Global Timestamp Barrier in ETL Topology
- As the coordinator of ETL Topology, MetaService interacts with source ETL job and generates a global
Timestamp Barrier
. Timestamp Barrier
is transmitted between ETL job nodes by tables, then these job nodes can create globally consistency snapshots in Table Store according to the barrier.
2> Manage dependencies between ETL jobs and tables
- MetaService manages the relationship between ETL jobs and tables in
ETL Topology
. Users can query these dependencies from MetaServices. - MetaService manages
Timestamp Barrier
in each ETL job, including barrier progress, completed barriers, etc. - MetaService manages the
Timestamp Barrier
and snapshot of tables, including the latest completed snapshots, the relationship between barriers and snapshots.
3> Manage Data Consistency in Query On Table Store
- MetaService supports data consistency in query based on the management of dependencies between tables.
MetaService determines the compaction and expiration of snapshots for each table according to snapshots being used by the OLAP and ETL jobs.
User Interfaces
User Interaction
Setup MetaService
In the first phase, we'd like to start a standalone MetaService with storage path and REST port in configuration.
User Cases
We add a new metastore type: table-store
, which manages the Catalog
and data consistency in Table Store. Users can create a Catalog with metastore table-store
in Sql-Client, and specify the address and consistency type by uri
and consistency-type
. Flink ETL job, which reads from and writes to Table Store will be managed by MetaService to ensure data consistency. In the first stage, table-store
metastore only supports FileSystemCatalog
and will support HiveCatalog
later. The user cases are shown as followed.
Code Block | ||
---|---|---|
| ||
-- create a catalog with MetaService
CREATE CATALOG my_catalog WITH (
'type'='table-store',
'warehouse'='file:/tmp/table_store',
'metastore' = 'table-store',
'uri'='http://<meta-service-host-name>:<port>',
'consistency'='ReadCommitted' );
USE CATALOG my_catalog;
-- create three user shopping tables in my_catalog which will be managed by MetaService
CREATE TABLE shopping (
userId BIGINT,
itemId BIGINT,
amount BIGINT,
price DOUBLE );
CREATE TABLE user_item_amount (
userId BIGINT,
itemId BIGINT,
totalAmount BIGINT );
CREATE TABLE user_item_price (
userId BIGINT,
itemId BIGINT,
totalPrice DOUBLE ); |
Users can
...
There are two core points in the architecture: Timestamp Barrier Mechanism
and MetaService
Timestamp Barrier Mechanism
We need a barrier mechanism in Flink to guarantee the data consistency.
- Each ETL source needs to be assigned a unified
Timestamp Barrier
- Stateful and temporal operators in Flink ETL job align and compute data according to the barrier.
- Sink operator in ETL job confirms data with barrier to sink tables in Table Store.
MetaService
component
MetaService
is the coordinator in ETL Topology
, its capabilities are as followed:
1> Coordinate the Global Timestamp Barrier in ETL Topology
- As the coordinator of ETL Topology, MetaService interacts with source ETL job and generates a global
Timestamp Barrier
. Timestamp Barrier
is transmitted between ETL job nodes by tables, then these job nodes can create globally consistency snapshots in Table Store according to the barrier.
2> Manage dependencies between ETL jobs and tables
- MetaService manages the relationship between ETL jobs and tables in
ETL Topology
. Users can query these dependencies from MetaServices. - MetaService manages
Timestamp Barrier
in each ETL job, including barrier progress, completed barriers, etc. - MetaService manages the
Timestamp Barrier
and snapshot of tables, including the latest completed snapshots, the relationship between barriers and snapshots.
3> Manage Data Consistency in Query On Table Store
- MetaService supports data consistency in query based on the management of dependencies between tables.
MetaService determines the compaction and expiration of snapshots for each table according to snapshots being used by the OLAP and ETL jobs.
User Interfaces
User Interaction
Setup MetaService
In the first phase, we'd like to start a standalone MetaService with storage path and REST port in configuration.
User Cases
We add a new metastore type: table-store
, which manages the Catalog
and data consistency in Table Store. Users can create a Catalog with metastore table-store
in Sql-Client, and specify the address and consistency type by uri
and consistency-type
. Flink ETL job, which reads from and writes to Table Store will be managed by MetaService to ensure data consistency. In the first stage, table-store
metastore only supports FileSystemCatalog
and will support HiveCatalog
later. The user cases are shown as followed.
Code Block | ||
---|---|---|
| ||
-- create a catalog with MetaService
CREATE CATALOG my_catalog WITH (
'type'='table-store',
'warehouse'='file:/tmp/table_store',
'metastore' = 'table-store',
'uri'='http://<meta-service-host-name>:<port>',
'consistency'='strong' );
USE CATALOG my_catalog;
-- create three tables in my_catalog which will be managed by MetaService
CREATE TABLE word_value (
word STRING PRIMARY KEY NOT ENFORCED,
val BIGINT );
CREATE TABLE word_count (
word STRING PRIMARY KEY NOT ENFORCED,
cnt BIGINT );
CREATE TABLE word_sum (
word STRING PRIMARY KEY NOT ENFORCED,
val_sum BIGINT ); |
Users can create a source table and three streaming jobs. The jobs write data to the three tables.
Code Block | ||
---|---|---|
| ||
-- create a wordshopping data generator table CREATE TEMPORARY TABLE wordshopping_tablesource ( worduserId STRINGBIGINT, valitemId BIGINT, ) WITH amount BIGINT, price DOUBLE ) WITH ( 'connector' = 'datagen', 'fields.word.length' = '14'); -- table store requires checkpoint interval in streaming mode SET 'execution.checkpointing.interval' = '10 s'; -- write streaming data to word_valueshopping, worduser_item_countamount and worduser_item_sumprice tables INSERT INTO word_valueshopping SELECT worduserId, itemId, amount, valprice FROM wordshopping_tablesource; INSERT INTO worduser_item_countamount SELECT worduser_id, item_id, countsum(*amount) FROM word_valueshopping GROUP BY word user_id, item_id; INSERT INTO worduser_item_sumprice SELECT word user_id, item_id, sum(valprice) FROM word_valueshopping GROUP BY word; user_id, item_id; |
The ETL Topology
is as followed
draw.io Diagram | ||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
Users can query data from the three tables.
Code Block | ||
---|---|---|
| ||
-- use tableau result mode SET 'sql-client.execution.result-mode' = 'tableau'; -- switch to batch mode RESET 'execution.checkpointing.interval'; SET 'execution.runtime-mode' = 'batch'; -- olap query the table SELECT T1.worduserId, T1.cnt as t1cnt,itemId T1.sum_valtotalAmount as t1sum_valamount, T2.cnttotalPrice as t2cntprice, T3T2.sum_valtotalPrice as t3sum_val FROM (SELECT word, count(*) as cnt, sum(val) as sum_val FROM word_value GROUP BY word) / T1.totalAmount as avgPrice FROM user_item_amount T1 JOIN worduser_item_countprice T2 JOIN word_sum T3 ON T1.worduserId=T2.worduserId and T2T1.worditemId=T3.worditemId; |
Since the data between jobs and tables is streaming, the results t1cnt and t2cnt, t1sum_val and t3sum_val are different without consistency guarantee amount, price and avgPrice are not correct; while MetaService
guarantees data consistency, the results t1cnt and t2cnt, t1sum_val and t3sum_val will be the same amount, price and avgPrice will be correct.
Query consistency information
...
Code Block | ||
---|---|---|
| ||
SELECT T.table_name FROM __META_JOB_SOURCE S JOIN __META_JOB_Sink T ON S.job_id=T.job_id WHERE S.table_name='Table1' |
Data Consistency Type
...
Timestamp Barrier
divides unbounded streaming data in ETL Topology
into multiple bounded data set, each bounded data set can be seen as a big transaction
in streaming processing. The key points of transaction are as follows
- If the records in a "epoch" (Timestamp Barrier) are finished writing to a table, we call the transaction is PROCESSED in the table.
- If the table creates a snapshot for the records in a "epoch", we call the transaction is WRITTEN in the table.
- If a transaction is PROCESSED in all tables, we call the transaction is PRECOMMIT
- If a transaction is WRITTEN in all tables, we call the transaction is COMMIT
When job fails, the records is not WRITTEN in a table will be "rolled back". Same as the above example, suppose the data in the tables are as follows
- user_item_amount: (user1, item1, 100)
- user_item_price: (user1, item1, 1000)
- shopping: (user1, item1, 200, 1500) with
Timestamp Barrier
T is processing by ETL jobs.
User performs query SELECT userId, itemId, totalPrice, totalAmount, totalPrice / totalAmount as avgPrice FROM UserItemAmount a JOIN UserItemPrice p ON a.userId=p.userId and a.itemId=p.itemId
on user_item_amount and user_item_price multiple times.
According to the characteristics of transaction, the following data consistency can be supported
- Read Uncommitted
Read Uncommitted refers to querying table data of uncommitted transactions. When some tables in a transaction have PROCESSED data, the remaining tables are not PROCESSED, and the transaction will not been PRECOMMIT yet. For example
- The data is PROCESSED in user_item_price: (user1, item1, 2500).
- The data is not PROCESSED in user_item_amount: (user1, item1, 100).
- The result of user's query will be (user1, item1, 2500, 100, 25) which is not a consistency result.
- Read Committed
Read Committed
refers to querying table data of PRECOMMIT transactions only. When a transaction
is PRECOMMIT, data in all tables are PROCESSED. Then the query can read the consistency data according to specific transaction
. For example
- The transaction T is not PRECOMMIT, the query result is (user1, item1, 1000, 100, 10)
- The transaction T has been PRECOMMIT, the query result is (user1, item1, 2500, 300, 8.33333)
Read Committed
doesn't support Repeatable Read
, which means when jobs fail after transaction T is PRECOMMIT, the data in tables will be rolled back and the query result will fallback from (user1, item1, 2500, 300, 8.33333) to (user1, item1, 1000, 100, 10)
- Repeatable Read
Repeatable Read
only reads data is WRITTEN in tables. The snapshots in a table won't be rolled back even when jobs fail. For example
- Transaction T has been PROCESSED, but the related snapshots in tables are not created, the query result is (user1, item1, 1000, 100, 10)
- When the related snapshots in tables have been created, the query result is (user1, item1, 2500, 300, 8.33333)
- Snapshots in a persistent storage won't be rolled back even when jobs fail, and the query result will always be (user1, item1, 2500, 300, 8.33333), it's
Repeatable Read
If Repeatable Read
only reads data of a COMMIT transaction, the data will be consistency; otherwise, the data in a query will be in different transaction.
Reuse Data In State
After align data with Timestamp Barrier
, join operators in jobs can keep Delta State
in memory and join data in shared state as follows
draw.io Diagram | ||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
Each join operator only need to store delta state with timestamp in memory, and when it needs to join data, it can get the total data from shared state and delta state
Query1:SELECT * FROM table1
Query2:SELECT * FROM table1 JOIN table2
Query3:SELECT * FROM table1 JOIN table2 JOIN table3
Strong Consistency
It will guarantee strong data consistency among queries above. Query gets the minimum version of all the related tables according to the source tables and the dependencies between them, which ensure data consistency between related tables. For the examples above, Query1, Query2 and Query3 will get Min(table1 version, table2 version) for table1 and table2, Min(table3 version) for table3.
Weak Consistency
It doesn't guarantee the data consistency among queries above, but only the data consistency of a single query. At this time, each query can get its latest version of tables, this ensures better data freshness.
Design of Data Consistency
...
- Stateless operator. The operator completely ignore the timestamp barrier, processes every input record and output the result which it just does before. It does not need to align data with
Timestamp Barrier
, and when it receivesTimestamp Barrier
, it should broadcast the barrier to downstream tasks. But it should collect all Timestamp Barrier and broadcast the barrier to downstream tasks. - Stateful/Temporal operator, should either
- If the business doesn't require ordering, it could process the records immediately as before
- If the business requires ordering, it buffers the records internally like current windowed/temporal operator are doing. Records in each "epoch" (as demarcated by timestamp barriers) will be processed after the previous "epoch" is finished, just like pre-aggregate
Timestamp Barrier
are out of order, stateful and temporal operators should align them according to their timestamp field. The operators will execute computation when they collect all the timestamp barrier, and broadcast it downstream tasks. There's a sequence relationship between timestamp barriers, and records between timestamp barriers are ordered- . It means that the operators compute and output results for a timestamp barrier based on the result of a previous timestamp barrier.
- Sink operator. Sink streaming output results to
Table Store
, and commit the results when it collects all the timestamp barrier. The source of downstream ETL job can prefetch data fromTable Store
, but should produce data after the upstream sink committed.- If the external system requires ordered writes (something like Kafka topic or append only store), the sinks would have to buffer the writes until a "timestamp barrier" arrives
- For sinks which might support writing the data simultaneously to different "epochs". For example writing files bucketed by each epoch. Each bucket/epoch could be committed independently
2. Timestamp Barrier
across ETL jobs
...
Component | Main Work | |
---|---|---|
MetaService |
| |
Table Store | Catalog |
|
Source and SplitEnumerator |
| |
Sink |
| |
Flink | Timestamp Barrier Mechanism | The detailed and main work is in the above table |
Planner |
| |
JobManager |
| |
Improvement |
|
Constraint
The current FLIP design has two constraints and it may continue to improve in the future
...
- Timestamp Barrier Coordination and Generation
- Timestamp Barrier Checkpoint and Recovery
- Timestamp Barrier Replay Data Implementation
- Timestamp Barrier Alignment and Computation In Operator
- Introduce Delta Join in Flink To Improve State Resource
- Introduce MetaService module and implement source/sink in Table Store and etc
- Job and Table management in MetaService such as exception handling, data revision and etc
...