DATE values describe a particular year/month/day in the form
yyyy-MM-dd. For example: DATE '2019-02-15'. DATE values do not have a
time of day component. The range of values supported for the DATE type
is 0000-01-01 to 9999-12-31.
This initial DATE type support covers TEXT and HBASE fileformats only.
'DateValue' is used as the internal type to represent DATE values.
The changes are as follows:
- Support for DATE literal syntax.
- Explicit casting between DATE and other types (note that invalid
casts will fail with an error just like invalid DECIMAL_V2 casts,
while failed casts to other types do no lead to warning or error):
- from STRING to DATE. The string value must be formatted as
yyyy-MM-dd HH:mm:ss.SSSSSSSSS. The date component is mandatory,
the time component is optional. If the time component is
present, it will be truncated silently.
- from DATE to STRING. The resulting string value is formatted as
yyyy-MM-dd.
- from TIMESTAMP to DATE. The source timestamp's time of day
component is ignored.
- from DATE to TIMESTAMP. The target timestamp's time of day
component is set to 00:00:00.
- Implicit casting between DATE and other types:
- from STRING to DATE if the source string value is used in a
context where a DATE value is expected.
- from DATE to TIMESTAMP if the source date value is used in a
context where a TIMESTAMP value is expected.
- Since STRING -> DATE, STRING -> TIMESTAMP and DATE -> TIMESTAMP
implicit conversions are now all possible, the existing function
overload resolution logic is not adequate anymore.
For example, it resolves the
if(false, '2011-01-01', DATE '1499-02-02') function call to the
if(BOOLEAN, TIMESTAMP, TIMESTAMP) version of the overloaded
function, instead of the if(BOOLEAN, DATE, DATE) version.
This is clearly wrong, so the function overload resolution logic had
to be changed to resolve function calls to the best-fit overloaded
function definition if there are multiple applicable candidates.
An overloaded function definition is an applicable candidate for a
function call if each actual parameter in the function call either
matches the corresponding formal parameter's type (without casting)
or is implicitly castable to that type.
When looking for the best-fit applicable candidate, a parameter
match score (i.e. the number of actual parameters in the function
call that match their corresponding formal parameter's type without
casting) is calculated and the applicable candidate with the highest
parameter match score is chosen.
There's one more issue that the new resolution logic has to address:
if two applicable candidates have the same parameter match score and
the only difference between the two is that the first one requires a
STRING -> TIMESTAMP implicit cast for some of its parameters while
the second one requires a STRING -> DATE implicit cast for the same
parameters then the first candidate has to be chosen not to break
backward compatibility.
E.g: year('2019-02-15') function call must resolve to
year(TIMESTAMP) instead of year(DATE). Note, that year(DATE) is not
implemented yet, so this is not an issue at the moment but it will
be in the future.
When the resolution algorithm considers overloaded function
definitions, first it orders them lexicographically by the types in
their parameter lists. To ensure the backward compatible behavior
Primitivetype.DATE enum value has to come after
PrimitiveType.TIMESTAMP.
- Codegen infrastructure changes for expression evaluation.
- 'IS [NOT] NULL' and '[NOT] IN' predicates.
- Common comparison operators (including the 'BETWEEN' operator).
- Infrastructure changes for built-in functions.
- Some built-in functions: conditional, aggregate, analytical and
math functions.
- C++ UDF/UDA support.
- Support partitioning and grouping by DATE.
- Beeswax, HiveServer2 support.
These items are tightly coupled and it makes sense to implement them
in one change-set.
Testing:
- A new partitioned TEXT table 'functional.date_tbl' (and the
corresponding HBASE table 'functional_hbase.date_tbl') was
introduced for DATE-related tests.
- BE and FE tests were extended to cover DATE type.
- E2E tests:
- since DATE type is supported for TEXT and HBASE fileformats
only, most DATE tests were implemented separately in
tests/query_test/test_date_queries.py.
Note, that this change-set is not a complete DATE type implementation,
but it lays the foundation for future work:
- Add date support to the random query generator.
- Implement a complete set of built-in functions.
- Add Parquet support.
- Add Kudu support.
- Optionally support Avro and ORC.
For further details, see IMPALA-6169.
Change-Id: Iea8155ef09557e0afa2f8b2d0b2dc9d0896dc30f
Reviewed-on: http://gerrit.cloudera.org:8080/12481
Reviewed-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
The .test file parser implemented an unconventional method for parsing
single-quoted strings in comma-separated value format. This didn't handle
trailing commas in the string correctly.
This commit switches to using a conventional method for parsing
comma-separated value format:
* Commas enclosed by single quotes are not treated as field separators
* Single quotes can be escaped within a string by doubling them.
I looked into using Python's .csv module for this, but it wouldn't
work without modifying the test file format more because it
automatically discards the quotes during parsing, which are actually
semantically important in .test files. E.g. without the quotes we can't
distinguish between the literal string 'regex:...' and the regex
regex:....
Testing:
Ran exhaustive tests and fixed .test files that required modifications.
Will rerun before merging.
Added a couple of tests to exercise edge cases in the test file parser.
Change-Id: I18ddcb0440490ddf8184be66d3681038a1615dd9
Reviewed-on: http://gerrit.cloudera.org:8080/11800
Reviewed-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Tested-by: Tim Armstrong <tarmstrong@cloudera.com>
Add support for creating a table based on a parquet file which contains arrays,
structs and/or maps.
Change-Id: I56259d53a3d9b82f318228e864c783b48a03f9ae
Reviewed-on: http://gerrit.cloudera.org:8080/582
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Internal Jenkins
This patch fixes the problem, that the Frontend would simply pass the
escaped value to the backend as an HBase filter and not the unescaped
one. Now queries including an escaped character will work as well.
Change-Id: I96e544973b523f3ef1abdec86ea1ec5596d9bee9
Reviewed-on: http://gerrit.cloudera.org:8080/520
Reviewed-by: Marcel Kornacker <marcel@cloudera.com>
Tested-by: Internal Jenkins
The following changes are included in this commit:
1. Modified the alltypesagg table to include an additional partition key
that has nulls.
2. Added a number of tests in hdfs.test that exercise the partition
pruning logic (see IMPALA-887).
3. Modified all the tests that are affected by the change in alltypesagg.
Change-Id: I1a769375aaa71273341522eb94490ba5e4c6f00d
Reviewed-on: http://gerrit.ent.cloudera.com:8080/2874
Reviewed-by: Dimitris Tsirogiannis <dtsirogiannis@cloudera.com>
Tested-by: jenkins
Reviewed-on: http://gerrit.ent.cloudera.com:8080/3236
The hbase-table-scanner used CallShortMethod to retrieve the size of the array.
Short will overflow. Because the size of the array is an int, we should use
CallIntMethod instead.
Change-Id: I941981f7504ee04adf998398f8baf6beae76d000
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1171
Reviewed-by: Alan Choi <alan@cloudera.com>
Tested-by: Alan Choi <alan@cloudera.com>
Tested-by: jenkins
The Impala CatalogService manages the caching and dissemination of cluster-wide metadata.
The CatalogService combines the metadata from the Hive Metastore, the NameNode,
and potentially additional sources in the future. The CatalogService uses the
StateStore to broadcast metadata updates across the cluster.
The CatalogService also directly handles executing metadata updates request from
impalad servers (DDL requests). It exposes a Thrift interface to allow impalads to
directly connect execute their DDL operations.
The CatalogService has two main components - a C++ server that implements StateStore
integration, Thrift service implementiation, and exporting of the debug webpage/metrics.
The other main component is the Java Catalog that manages caching and updating of of all
the metadata. For each StateStore heartbeat, a delta of all metadata updates is broadcast
to the rest of the cluster.
Some Notes On the Changes
---
* The metadata is all sent as thrift structs. To do this all catalog objects (Tables/Views,
Databases, UDFs) have thrift struct to represent them. These are sent with each statestore
delta update.
* The existing Catalog class has been seperated into two seperate sub-classes. An
ImpladCatalog and a CatalogServiceCatalog. See the comments on those classes for more
details.
What is working:
* New CatalogService created
* Working with statestore delta updates and latest UDF changes
* DDL performed on Node 1 is now visible on all other nodes without a "refresh".
* Each DDL operation against the Catalog Service will return the catalog version that
contains the change. An impalad will wait for the statestore heartbeat that contains this
version before returning from the DDL comment.
* All table types (Hbase, Hdfs, Views) getting their metadata propagated properly
* Block location information included in CS updates and used by Impalads
* Column and table stats included in CS updates and used by Impalads
* Query tests are all passing
Still TODO:
* Directly return catalog object metadata from DDL requests
* Poll the Hive Metastore to detect new/dropped/modified tables
* Reorganize the FE code for the Catalog Service. I don't think we want everything in the
same JAR.
Change-Id: I8c61296dac28fb98bcfdc17361f4f141d3977eda
Reviewed-on: http://gerrit.ent.cloudera.com:8080/601
Reviewed-by: Lenni Kuff <lskuff@cloudera.com>
Tested-by: Lenni Kuff <lskuff@cloudera.com>
* Changed frontend analysis for HBase tables
* Changed Thrift messages to allow HBase as a sink type.
* JNI Wrapper around htable
* Create hbase-table-sink
* Create hbase-table-writer
* Static init lots of JNI related code for HBase.
* Cleaned up some cpplint issues.
* Changed junit analysis tests
* Create a new HBase test table.
* Added functional tests for HBase inserts.
