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This patch adds the ability for the test infrastructure's Impala-to-PostgreSQL data migration tool to recognize whether the Impala source tables have primary keys, and if so, CREATE the tables in PostgreSQL with the same primary keys. This is needed especially for performing CRUD operations by the random query generator for comparison with Impala/Kudu tables and equivalent PostgreSQL tables. I modified the make_create_table_sql() implementation to check the "universal" Python object model of the table's columns. We generate CREATE TABLE statements with, or without, a PRIMARY KEY clause. For Impala-side tables that this tool may create, we also ensure that we only write such a clause when the table's format supports primary keys (currently Kudu). When the random query generator runs, it needs to know that the tables it's examining in both databases are equivalent. It does this by examining the tables' names, column names, and column types. I have added whether the column is a primary key as part of this equivalence test. Testing: - The patch includes some unit and system tests for the tool. - Actually migrated a few small Kudu and HDFS tables from Impala into both PostgreSQL 9.3 and 9.5 and examined the tables in PostgreSQL to make sure they had primary keys (or not) as expected. - Very short discrepancy_searcher.py --explain-only runs to test positive and negative cases of Impala/PostgreSQL equivalency. Change-Id: I447f022e2dc3d4fc8373b7f388c7875a869921b8 Reviewed-on: http://gerrit.cloudera.org:8080/4951 Reviewed-by: Taras Bobrovytsky <tbobrovytsky@cloudera.com> Tested-by: Internal 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.