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Minor compactions can compact several delta directories into a single
delta directory. The current directory filtering algorithm had to be
modified to handle minor compacted directories and prefer those over
plain delta directories. This happens in the Frontend, mostly in
AcidUtils.java.
Hive Streaming Ingestion writes similar delta directories, but they
might contain rows Impala cannot see based on its valid write id list.
E.g. we can have the following delta directory:
full_acid/delta_0000001_0000010/0000 # minWriteId: 1
# maxWriteId: 10
This delta dir contains rows with write ids between 1 and 10. But maybe
we are only allowed to see write ids less than 5. Therefore we need to
check the ACID write id column (named originalTransaction) to determine
which rows are valid.
Delta directories written by Hive Streaming don't have a visibility txn
id, so we can recognize them based on the directory name. If there's
a visibilityTxnId and it is committed => every row is valid:
full_acid/delta_0000001_0000010_v01234 # has visibilityTxnId
# every row is valid
If there's no visibilityTxnId then it was created via Hive Streaming,
therefore we need to validate rows. Fortunately Hive Streaming writes
rows with different write ids into different ORC stripes, therefore we
don't need to validate the write id per row. If we had statistics,
we could validate per stripe, but since Hive Streaming doesn't write
statistics we validate the write id per ORC row batch (an alternative
could be to do a 2-pass read, first we'd read a single value from each
stripe's 'currentTransaction' field, then we'd read the stripe if the
write id is valid).
Testing
* the frontend logic is tested in AcidUtilsTest
* the backend row validation is tested in test_acid_row_validation
Change-Id: I5ed74585a2d73ebbcee763b0545be4412926299d
Reviewed-on: http://gerrit.cloudera.org:8080/15818
Reviewed-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
This directory contains Impala test data sets. The directory layout is structured as follows:
datasets/
<data set>/<data set>_schema_template.sql
<data set>/<data files SF1>/data files
<data set>/<data files SF2>/data files
Where SF is the scale factor controlling data size. This allows for scaling the same schema to
different sizes based on the target test environment.
The schema template SQL files have the following format:
The goal is to provide a single place to define a table + data files
and have the schema and data load statements generated for each combination of file
format, compression, etc. The way this works is by specifying how to create a
'base table'. The base table can be used to generate tables in other file formats
by performing the defined INSERT / SELECT INTO statement. Each new table using the
file format/compression combination needs to have a unique name, so all the
statements are pameterized on table name.
The template file is read in by the 'generate_schema_statements.py' script to
to generate all the schema for the Imapla benchmark tests.
Each table is defined as a new section in the file with the following format:
====
---- SECTION NAME
section contents
...
---- ANOTHER SECTION
... section contents
---- ... more sections...
Note that tables are delimited by '====' and that even the first table in the
file must include this header line.
The supported section names are:
DATASET
Data set name - Used to group sets of tables together
BASE_TABLE_NAME
The name of the table within the database
CREATE
Explicit CREATE statement used to create the table (executed by Impala)
CREATE_HIVE
Same as the above, but will be executed by Hive instead. If specified,
'CREATE' must not be specified.
CREATE_KUDU
Customized CREATE TABLE statement used to create the table for Kudu-specific
syntax.
COLUMNS
PARTITION_COLUMNS
ROW_FORMAT
HBASE_COLUMN_FAMILIES
TABLE_PROPERTIES
HBASE_REGION_SPLITS
If no explicit CREATE statement is provided, a CREATE statement is generated
from these sections (see 'build_table_template' function in
'generate-schema-statements.py' for details)
ALTER
A set of ALTER statements to be executed after the table is created
(typically to add partitions, but may also be used for other settings that
cannot be specified directly in the CREATE TABLE statement).
These statements are ignored for HBase and Kudu tables.
LOAD
The statement used to load the base (text) form of the table. This is
typically a LOAD DATA statement.
DEPENDENT_LOAD
DEPENDENT_LOAD_KUDU
DEPENDENT_LOAD_HIVE
DEPENDENT_LOAD_ACID
Statements to be executed during the "dependent load" phase. These statements
are run after the initial (base table) load is complete.
HIVE_MAJOR_VERSION
The required major version of Hive for this table. If the major version
of Hive at runtime does not exactly match the version specified in this section,
the table will be skipped.
NOTE: this is not a _minimum_ version -- if HIVE_MAJOR_VERSION specifies '2',
the table will _not_ be loaded/created on Hive 3.