Igor Rudyak created IGNITE-3933:

             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 

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 
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 

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