082 rows = 1 loops = 1 ) Planning Time : 0. 016 rows = 3 loops = 1 ) -> CTE Scan on to_soft_delete ( actual time = 1. 099 rows = 4 loops = 1 ) -> CTE Scan on to_upsert ( actual time = 0. 110 rows = 4 loops = 1 ) Group Key : to_upsert. 001 rows = 3 loops = 1 ) -> HashAggregate ( actual time = 1. 081 rows = 1 loops = 1 ) Index Cond : ( k1 = 0 ) Filter : ( NOT ( hashed SubPlan 3 )) Rows Removed by Filter : 3 SubPlan 3 -> CTE Scan on payload payload_2 ( actual time = 0. 014 rows = 3 loops = 1 ) CTE to_soft_delete -> Index Scan using demo_pkey on demo demo_1 ( actual time = 1. 012 rows = 3 loops = 1 ) CTE to_upsert -> CTE Scan on payload payload_1 ( actual time = 0. 787 rows = 0 loops = 1 ) Conflict Resolution : UPDATE Conflict Arbiter Indexes : demo_pkey Tuples Inserted : 0 Conflicting Tuples : 4 CTE payload -> Function Scan on jsonb_to_recordset payload ( actual time = 0. Yugabyte =# explain ( costs off, analyze ) execute etl ( 0, $$ $$ :: jsonb ) QUERY PLAN - Insert on demo ( actual time = 11. Here is a simple example declaring my table: In PostgreSQL version 12, the ability to query json and jsonb data types directly using a newly added JSON data type named jsonpath was released, making it even easier to interact with. UPDATE behavior can do the same with clean and simple SQL. There are differences, though: MongoDB limits its BSON format to a maximum of 64 bits for representing an integer or floating point number. In this post, well describe JSON datatype in brief and discuss its. First, to be clear, Postgres and MongoDB both have functions for JSON and JSONB data storage (although MongoDB calls the latter BSON). And with PostgreSQL compatibility, JSON processing and INSERT. However, there are few which deal with indexes for JSON type and their performance. With a distributed database like YugabyteDB, it is more efficient to process this in the database, because that can scale (we can connecto to any node), run in one server-side transaction (easier to handle transparent retries in case of clock skew) and reduce the roundtrips (working on batches of rows). Their current application uses Python Pandas to retrieve the current records, compare them with the new paysload, and write it back to the database. I add a timestamp ts timestamptz to record the last change time.missing IDs will me marked as deleted ( deleted boolean).the application receives a payload, in JSON, describing the new data.a transaction table holds an ID ( k1 int in my example) for with there are multiple records ( k2 int for their key and v1 int, v2 int for their value in my small example).A user exposed his "ETL" use-case as the following:
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