Igor Rudyak created IGNITE-3933:
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Summary: JDBC issue with Replicated & Partitioned caches
Key: IGNITE-3933
URL: https://issues.apache.org/jira/browse/IGNITE-3933
Project: Ignite
Issue Type: Bug
Components: jdbc-driver, odbc, SQL
Affects Versions: 1.8
Reporter: Igor Rudyak
Priority: Critical
There is an issue with JDBC when trying to play with different types of caches
using the same JDBC connection.
Example 1 - using simple JDBC connection URL
jdbc:ignite:cfg://file:///my-ignite-client-config.xm
1) For REPLICATED caches SQL query like "select * from my_replicated_cache"
returns as many duplicates for each record as many nodes in an Ignite cluster.
Same problem with "select count(*) from my_replicated_cache" - it returns
actual number of records multiplied by the number of Ignite nodes.
2) At the same time if traversing the cache using "for" loop and iterator, it
returns exactly what's needed without any duplicates.
Example 2 - specifying replicated or partitioned cache in JDBC connection URL
jdbc:ignite:cfg://cache=my_cache@file:///my-ignite-client-config.xm
1) If specifying PARTITIONED cache in JDBC URL - queries like "select * from
my_replicated_cache" return duplicates
2) If specifying REPLICATED cache in JDBC URL - it doesn't return duplicates
for the "select * from my_replicated_cache" query. At the same time it failed
to execute simple queries like "select * from my_partitioned_cache" against
PARTITIONED caches throwing this error:
java.lang.RuntimeException: javax.cache.CacheException: Queries running on
replicated cache should not contain JOINs with partitioned tables
[rCache=product, pCache=order]
The fact that it's not possible to combine REPLICATED and PARTITIONED caches in
one SQL query (using one JDBC connection) looks not very good.
Also the idea of specifying cache name (for REPLICATED cache) in the JDBC URL
for optimization purposes doesn't look good. It's better to utilize rather wide
used "hits" approach, to provide optimization hints inside SQL query. Otherwise
it's not possible to use JDBC with analytical and UI tools to run ad-hock SQL
queries.
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