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Prototype of HdfsJsonScanner implemented based on rapidjson, which supports scanning data from splitting json files. The scanning of JSON data is mainly completed by two parts working together. The first part is the JsonParser responsible for parsing the JSON object, which is implemented based on the SAX-style API of rapidjson. It reads data from the char stream, parses it, and calls the corresponding callback function when encountering the corresponding JSON element. See the comments of the JsonParser class for more details. The other part is the HdfsJsonScanner, which inherits from HdfsScanner and provides callback functions for the JsonParser. The callback functions are responsible for providing data buffers to the Parser and converting and materializing the Parser's parsing results into RowBatch. It should be noted that the parser returns numeric values as strings to the scanner. The scanner uses the TextConverter class to convert the strings to the desired types, similar to how the HdfsTextScanner works. This is an advantage compared to using number value provided by rapidjson directly, as it eliminates concerns about inconsistencies in converting decimals (e.g. losing precision). Added a startup flag, enable_json_scanner, to be able to disable this feature if we hit critical bugs in production. Limitations - Multiline json objects are not fully supported yet. It is ok when each file has only one scan range. However, when a file has multiple scan ranges, there is a small probability of incomplete scanning of multiline JSON objects that span ScanRange boundaries (in such cases, parsing errors may be reported). For more details, please refer to the comments in the 'multiline_json.test'. - Compressed JSON files are not supported yet. - Complex types are not supported yet. Tests - Most of the existing end-to-end tests can run on JSON format. - Add TestQueriesJsonTables in test_queries.py for testing multiline, malformed, and overflow in JSON. Change-Id: I31309cb8f2d04722a0508b3f9b8f1532ad49a569 Reviewed-on: http://gerrit.cloudera.org:8080/19699 Reviewed-by: Quanlong Huang <huangquanlong@gmail.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 Impala 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.