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UPSERTs are very similar to INSERTs, so the UPSERT support is simply folded into that of INSERT. We do this by adding another "conflict action", CONFLICT_ACTION_UPDATE. The object responsible for holding the conflict_action attribute is now the InsertClause. This is needed here because the SqlWriter now needs to know the conflict_action both when writing the InsertClause (Impala) and at the tail end of the InsertStatement (PostgreSQL). We also add a few properties to the InsertStatement interface so that the PostgresqlSqlWriter can form the correct "DO UPDATE" conflic action, in which primary key columns and updatable columns must be known. More information on that here: https://www.postgresql.org/docs/9.5/static/sql-insert.html By default, we will tend to generate 3 UPSERTs for every 1 INSERT. In addition to adding unit tests to make sure UPSERTs are properly written, I used discrepancy_searcher.py --profile dmlonly, both with and without --explain-only, do run tests. I made sure we were generating syntactically valid UPSERT statements, and that the INSERT/UPSERT ratio was roughly 1/3 after 100 statements. Change-Id: I6382f6ab22ba29c117e39a5d90592d3637df4b25 Reviewed-on: http://gerrit.cloudera.org:8080/5795 Reviewed-by: Taras Bobrovytsky <tbobrovytsky@cloudera.com> Tested-by: Impala Public Jenkins
Purpose:
This package is intended to augment the standard test suite. The standard tests are
more efficient with regards to features tested versus execution time. However their
coverage as a test suite still leaves gaps in query coverage. This package provides a
random query generator to compare the results of a wide range of queries against a
reference database engine. The queries will range from very simple single table selects to
extremely complicated with multiple level of nesting. This method of testing will be
slower but has a larger coverage area.
Requirements:
1) It's assumed that Impala is running locally.
2) Impyla -- an implementation of DB API 2 for Impala.
sudo pip install impyla
3) At least one python driver for a reference database.
sudo apt-get install python-mysqldb
sudo apt-get install python-psycopg2 # Postgresql
For Impala/Kudu CRUD random query generation and comparison, please see
the supplemental POSTGRES.txt on setting up PostgresQL 9.5 or higher as
a reference database.
Please see the supplemental ORACLE.txt on setting up Oracle as a reference
database.
Usage:
1) Generate test data
./data_generator.py --use-postgresql
This will generate tables and data in Postgresql and Impala
2) Run the comparison
./discrepancy_searcher.py
This will generate queries using the test database and compare the results against
Postgresql (the default).
Things to Know:
1) A good number of queries to run seems to be about 5k. Ideally each test run would
discover the complete list of known issues. From experience a 1k query test run may
complete without finding any issues that were discovered in previous runs. 5k seems
to be about the magic number were most issues will be rediscovered. This can take 1-2
hours. However as of this writing it's rare to run 1k queries without finding at
least one discrepancy.
2) It's possible to provide a randomization seed so that the randomness is actually
reproducible. The data generation currently has a default seed so will always produce
the same tables. This also mean if a new data type is added those generated tables
will change.
3) There is a query log. It's possible that a sequence of queries is required to expose
a bug. If you come across a failure that can't be reproduced by rerunning the failed
query, try running the queries leading up to that query as well.
Miscellaneous:
1) Instead of generating new random queries with each run, it may be better to reuse a
list of queries from a previous run that are known to produce results. As of this
writing only about 50% of queries produce results. So it may be better to trade high
randomness for higher quality queries. For example it would be possible to build up a
library of 100k queries that produce results then randomly select 2.5k of those.
Maybe that would provide testing equivalent to 5k totally random queries in less
time.
This would also be useful in eliminating queries that have known issues above.
Postgresql:
1) Supports basically all Impala language features. Exceptions include:
a) IGNORE NULLS clause for analytic functions
2) Has strange sorting of strings, '-1' > '1'. This may be important if ORDER BY is ever
used. The databases being compared would need to have the same collation, which is
probably configurable.
MySQL:
1) Does not support analytics.
2) Has poor boolean support.
Oracle:
1) No Boolean support.
2) Better analytic function support, e.g. "IGNORE NULLS" is supported.
3) Strange oddities abound, like no LIMIT clause.
Improvements:
1) Add the ability to incrementally increase the complexity of query profiles
automatically during execution. For example, the profile could start with no joins,
then after 100 or so queries, the number of joins could be increased. This would
lead to bugs that are not related to joins being found by much simpler queries.
2) Add support for simplifying buggy queries. When a random query fails the comparison
check it is basically always much too complex for directly posting a bug report. It
is also time consuming to simplify the queries because there is a lot of trial and
error and manually editing queries.
3) Add common built-in functions. Ex: NVL, ...
4) Add support for comparing results with codegen enabled and disabled. Uri recently added
support for query options in Impyla.