I am trying to load the sales data to the database using Azure Synapse Analytics pipelines, and the strategy is as follows (scenario is made up):
Load the students` data to the table Students
Load the students` classes information to the table StudentsClasses. In this data flow I need to join the data with the Students table (obviously, the new data about students must be loaded to Students at this join step)
Can I have these two processes in the same data flow with Sink ordering? Or does the sink ordering not define source read ordering? (that is, the source reading and transformations are done in parallel, and only the write is according to the ordering?
Edit: This is an example data flow that I want to implement:
source3 and sink1 are the same table. What I want is to first populate sink1, then use it for source 2 to join with it. Can this be implemented using Sink ordering? Or source3 will be empty regardless of sink ordering?
Yes, you can use multiple source and sinks in a single data flow and reference same source over join activity. And order sink write using Custom sink ordering property
I am using Inline dataset but you can use any type
Using inline dataset to store the result in sink1. In source3, use the same inline dataset to join with Source2
Make sure you give the sink order correctly, if you have the wrong order or if it encounters no data while proceeding with transformation, it will publish with no errors however the pipeline run would fail.
Refer MS DOC: Sink ordering
Related
I am using KStreams where I need to de-duplicate the data. Source ingests duplicated data due to many reasons i.e data itself duplicate, re-partitioning.
Currently using Redis for this use-case where data is stored something as below
id#object list-of-applications-processed-this-id-and-this-object
As KSQL is implemented on top of RocksDB which is also a Key-Value database, can I use KSql for this use case?
At the time of successful processing, I would add an entry to KSQL. At the time of reception, I will have to check the existence of the id in KSQL.
Is it correct use case as per KSql design in the event processing world?
If you want to use to use ksqlDB as a cache, you can create a TABLE using the topic as data source. Note that a CREATE TABLE statement by itself, does only declare a schema (it does not pull in any data into ksqlDB yet).
CREATE TABLE inputTable <schemaDefinition> WITH(kafka_topic='...');
Check out the docs for more details: https://docs.ksqldb.io/en/latest/developer-guide/ksqldb-reference/create-table/
To pull in the data, you can create a second table via:
CREATE TABLE cache AS SELECT * FROM inputTable;
This will run a query in the background, that read the input data and puts the result into the ksqlDB server. Because the query is a simple SELECT * it effectively pulls in all data from the topic. You can now issue "pull queries" (i.e, lookups) against the result to use TABLE cache as desired: https://docs.ksqldb.io/en/latest/developer-guide/ksqldb-reference/select-pull-query/
Future work:
We are currently working on adding "source tables" (cf https://github.com/confluentinc/ksql/pull/7474) that will make this setup simpler. If you declare a source table, you can do the same with a single statement instead of two:
CREATE SOURCE TABLE cache <schemaDefinition> WITH(kafka_topic='...');
I would like to create Tables in ksqlDB from Debezium source topics, with the ultimate aim of performing a left join on these tables and efficiently outputting materialized views to a downstream database using the JDBC sink connector.
The Debezium source topics have not had any transforms applied (such as ExtractNewRecordState), so contain a 'before' and 'after' property, as described in the Debezium documentation here.
The reason for not applying the ExtractNewRecordState transform (which would presumably simplify matters) is that the source CDC topics may be used for various purposes and it does not appear possible to create multiple topics off the same source database table (since topic names are automatically determined by Debezium and depend on the database server, schema and table name as described here).
The best approach I have found so far is to:
create a stream in ksqlDB from the raw Debezium input, e.g.:
CREATE STREAM user_stream WITH (KAFKA_TOPIC='mssql.dbo.user', VALUE_FORMAT='AVRO');
create a second stream selecting the required fields from the 'after' property of the first stream, e.g.:
CREATE STREAM user_stream2 AS SELECT AFTER->user_id, AFTER->username, AFTER->email FROM user_stream EMIT CHANGES;
finally, convert the second stream to a table as described here, namely:
SELECT user_id,
LATEST_BY_OFFSET(username) AS username,
LATEST_BY_OFFSET(email) AS email
FROM user_stream2
GROUP BY user_id
EMIT CHANGES;
These steps must be repeated to generate each Table, at which point a join can be performed on the Tables to produce an output.
This seems quite long-winded, with a lot of intermediate steps. Performance also seems sluggish. Is there a better and/or more direct way to generated materialized views using ksqlDB and Debezium? Can any of the steps be cut out and/or should I be using a different approach in step 3 (such as a windowing function)?
I'm particularly keen to ensure that the approach taken is the most efficient from a performance and resource usage perspective.
I have two tables in snowflake named table1 and table2. Table1 is the source table which contains incremental data and table2 is the target table.
So my usecase is I have to take data from table1 and update the data into table2 but this process has to be done using Azure Data Factory.
I tried to create a data flow in ADF but it didn't allowed me to connect with the snowflake directly as it is not in the supported sources list. The native snowflake connector only supports the Copy Data Activity. So as a work around I first created a copy activity which copy the data from snowflake to azure blob. Then used the Azure Blob as source for Data Flow to create my scd1 implementation and saved the output in csv files.
Now My question is how should I update the data in target table2. Because If I directly use the copy activity to copy the csv files into snowflake then it will result in the duplicate records at snowflake side. For instance lets say table2 contains a row
id,name,age,data
1234,kristopher,24,somedata
and table1 contains
id,name,age,data
1234,kristopher,24,some-new-data
So now I have table1 data in csv which has to be loaded in snowflake. If I am loading directly then the resultant looks something like this.
id,name,age,data
1234,kristopher,24,somedata
1234,kristopher,24,some-new-data
But I only need
1234,kristopher,24,some-new-data
Let me know if some more explanation is required. I am new to Azure Data Factory and Snowflake as well.
Thanks
As you have observed, the ADF Data Flows currently don't support Snowflake datasets as a source.
You could theoretically follow this design pattern but it seems like alot of work for the requirement you have described. An alternative would be to go down the Azure Function route, but again I would trade off the requirement vs. effort required.
If it didn't have to be in ADF, then a quick approach would be to use a Snowflake Task to schedule some SQL to manage the SCD behavior for you.
I hope this helps.
Best regards,
Dan.
you can put your login in a snowflake stored procedure, then execute your stored proc in ADF
I have a copy data activity for on-premise SQL Server as source and ADLS Gen2 as sink. There is a control table to pickup tableName, watermarkDateColumn and the watermarkDatetime to pull incremental data from the source database.
After data is pulled/loaded in sink, I want to get the max of the watermarkDateColumn in my dataset. Can it be obtained from #activity('copyActivity1').output?
I'm not allowed to use one extra lookup activity to query the source table for getting the max(watermarkDateColumn) in pipeline.
Copy activity only could be used for data transmission,not for any other aggregation feature. So #activity('copyActivity1').output won't help. Since you said you can't use lookup activity, i'm afraid your requirement is not available so far.
If you prefer not using additional activities, I suggest you using Data Flow Activity instead which is more flexible.There is built-in aggregation feature in the Data Flow Activity.
I am currently working on a project which needs to ingest data from a Kafka Topic (JSON format), and write it directly into Clickhouse. I followed the method as suggested in the Clickhouse documentation:
Step 1: Created a clickhouse consumer which writes into a table (say, level1).
Step 2: I performed a select query on 'level1' and it gives me a set of results, but is not particularly useful as it can be read only once.
Step 3: I created a materialised view that converts data from the engine(level1) and puts it into a previously created table (say, level2). While writing into 'level2' the aggregation is on a day level (done by converting timestamp in level1 to datetime).
Therefore, data in 'level2' :- day + all columns in 'level1'
I intend to use this view (level2) as the base for any future aggregation (say, at level3)
Problem 1: 'level2' is being created but data is not being populated in it, i.e., when I perform a basic select query (select * from level2 limit 10) on the view, the output is "0 rows in set".
Is it because of day level aggregation, and it might populate at the end of the day? Can I ingest data from 'level2' in real-time?
Problem 2: Is there a way of reading the same data from my engine 'level1', multiple times?
Problem 3: Is there a way to convert Avro to JSON while reading from a kafka topic? Or can Clickhouse write data (in Avro format) directly into 'level1' without any conversion?
EDIT: There is latency in Clickhouse while retrieving data from Kafka. Had to make changes in the user.xml file in my Clickhouse server (change max_block_size).
Problem 1: 'level2' is being created but data is not being populated in it, i.e., when I perform a basic select query (select * from level2 limit 10) on the view, the output is "0 rows in set".
This might be related to the default settings of kafka storage, which always starts consuming data from the latest offset. You can change the behavior by adding this
<kafka>
<auto_offset_reset>earliest</auto_offset_reset>
</kafka>
to config.xml
Problem 2: Is there a way of reading the same data from my engine 'level1', multiple times?
You'd better avoid reading from kafka storage directly. You can set up a dedicated materialized view M1 for 'level1' and use that to populate 'level2' too. Then reading from M1 is repeatable.
Problem 3: Is there a way to convert Avro to JSON while reading from a kafka topic? Or can Clickhouse write data (in Avro format) directly into 'level1' without any conversion?
Nope, though you can try using Cap'n Proto which should provide similar performance like Avro, and it's supported directly by ClickHouse.