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https://issues.apache.org/jira/browse/SPARK-50303?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17898043#comment-17898043
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Eric Sun edited comment on SPARK-50303 at 11/13/24 8:26 PM:
------------------------------------------------------------
For example, we can use a simple-yet-effective JSON as QUERY_TAG to associate a
series individual SQL statements into a single business context:
{code:json}
{"namespace":"airflow-prod-03", "project":"payments", "flow":"abc",
"job":"sub-flow/job_name", "batch_id":"2024-10-12T01:30:00", "retry":3,
"owner":"john.smith", "cost_center":"finance",
"start_dt":"2024-10-09T00:00:00", "end_dt":"2024-10-12T00:00:00"}
{code}
was (Author: ericsun2):
For example, we can use a simple-yet-effective JSON as QUERY_TAG to associate a
series individual SQL statements into a single business context:
{code:json}
{"namespace":"airflow-prod-03", "project":"payments", "flow":"abc",
"job":"sub-flow/job_name", "batch_id":"2024-10-12T01:30:00", "retry":3,
"owner":"john.smith", "cost_center":"finance",
"start_dt":"2024-10-09T00:00:00", "end_dt":"2024-10-12T00:00:00"}
\{code}
> Enable QUERY_TAG for SQL Session in Spark SQL
> ---------------------------------------------
>
> Key: SPARK-50303
> URL: https://issues.apache.org/jira/browse/SPARK-50303
> Project: Spark
> Issue Type: Wish
> Components: SQL
> Affects Versions: 4.0.0, 3.5.3
> Reporter: Eric Sun
> Priority: Critical
> Labels: SQL, Tagging, observability
>
> As Spark SQL becomes more powerful for both analytics and ELT (with big T),
> we see more tools are generating and executing SQL to transform data.
> *Session* is a very important mechanism for lineage and usage/cost tracking,
> especially for the multi-statement ELT cases. *Tagging* a
> {color:#ff0000}series{color} of query statements with the higher level
> business *context* (such as project, flow_name, job_name, batch_id,
> start_data_dt, end_data_dt, owner, cost_group, ...) can provide tremendous
> observability improvement without much overhead. It is not efficient to
> collect and analyze the scattered query UUID and try to group them together
> to reconstruct the SESSION. But it is quite easy to allow the SQL client to
> set the tags when the session is established.
> * Presto has *Session Properties*
> * Trino has {*}X-Trino-Session{*}, *X-Trino-Client-Info* and
> *X-Trino-Client-Tags* to carry a list of K/V
> * Snowflake has *QUERY_TAG* to make observability much easier and efficient
> * Redshift supports tagging for query as well
> It will be great that Spark SQL can set a paved path/recipe for the
> workload/cost analysis/observability based on the session QUERY_TAG, so that
> the whole community can follow instead reinventing the wheel.
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