Syntax is "CREATE TABLE name LIKE fileformat '/path/to/file'".
Supports all options that CREATE TABLE does. Currently only PARQUET is supported.
Run testdata/bin/create-load-data.sh after pulling this patch.
Change-Id: Ibb9fbb89dbde6acceb850b914c48d12f22b33f55
Reviewed-on: http://gerrit.ent.cloudera.com:8080/2720
Reviewed-by: Victor Bittorf <victor.bittorf@cloudera.com>
Tested-by: jenkins
Reviewed-on: http://gerrit.ent.cloudera.com:8080/3158
This patch checks the test-warehouse's stored githash (if it exists) to determine if the
current patch has changed the schema if a table. If a change is detected, we force load
all the data.
Change-Id: I314f9f3364d3e6b2d66de38a9e6d9f57c4e279a7
Reviewed-on: http://gerrit.ent.cloudera.com:8080/3049
Reviewed-by: Ishaan Joshi <ishaan@cloudera.com>
Tested-by: jenkins
This change adds DDL support for HDFS caching. The DDL allows the user to indicate a
table or partition should be cached and which pool to cache the data into:
* Create a cached table: CREATE TABLE ... CACHED IN 'poolName'
* Cache a table/partition: ALTER TABLE ... [partitionSpec] SET CACHED IN 'poolName'
* Uncache a table/partition: ALTER TABLE ... [partitionSpec] SET UNCACHED
When a table/partition is marked as cached, a new HDFS caching request is submitted
to cache the location (HDFS path) of the table/partition and the ID of that request
is stored with in the table metadata (in the table properties). This is stored as:
'cache_directive_id'='<requestId>'. The cache requests and IDs are managed by HDFS
and persisted across HDFS restarts.
When a cached table or partition is dropped it is important to uncache the cached data
(drop the associated cache request). For partitioned tables, this means dropping all
cache requests from all cached partitions in the table.
Likewise, if a partitioned table is created as cached, new partitions should be marked
as cached by default.
It is desirable to know which cache pools exists early on (in analysis) so the query
will fail without hitting HDFS/CatalogServer if a non-existent pool is specified. To
support this, a new cache pool catalog object type was introduced. The catalog server
caches the known pools (periodically refreshing the cache) and sends the known pools out
in catalog updates. This allows impalads to perform analysis checks on cache pool
existence going to HDFS. It would be easy to use this to add basic cache pool management
in the future (ADD/DROP/SHOW CACHE POOL).
Waiting for the table/partition to become cached may take a long time. Instead of
blocking the user from access the time during this period we will wait for the cache
requests to complete in the background and once they have finished the table metadata
will be automatically refreshed.
Change-Id: I1de9c6e25b2a3bdc09edebda5510206eda3dd89b
Reviewed-on: http://gerrit.ent.cloudera.com:8080/2310
Reviewed-by: Lenni Kuff <lskuff@cloudera.com>
Tested-by: jenkins
When updating partition metadata as part of COMPUTE STATS we would previously
attempt to update all partitions at once. This could lead to HMS socket timeouts
and also could run into issues if there were > 32K partitions.
In this change we now update the partitions in batches, with a max size of 500
partitions per batch. We also compare whether the row count has changed and only
update partitions that have been modified.
Change-Id: If7bfcc30f86fc2fdd79855b981067ac29a47b5e1
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1913
Reviewed-by: Lenni Kuff <lskuff@cloudera.com>
Tested-by: jenkins
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1918
This updates how Impala fetches partition metadata from the Hive Metastore to fetch
partitions in batches, rather than all at once. This helps reduce the load on the
HMS and also lets Impala scale to above 32K partitions. The downside is that it
may require additional RPCs to get all the partitions.
This is done by first querying the metastore to get all the partition names that
exist, then splitting the list of names into seperate batches to get the actual
partition metadata.
Impala uses a default size of 1000 partitions per batch, but it can be configured
by setting the 'hive.metastore.batch.retrieve.table.partition.max' parameter
in the hive-site.xml config file.
Change-Id: Ide0ec30ef8a9e00f79c26551aa8e5e7814c73034
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1662
Reviewed-by: Lenni Kuff <lskuff@cloudera.com>
Tested-by: jenkins
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1698
Impala reserves resources from YARN via Llama and handles resources
preemptions by cancelling affected queries. Adds the Impala Resource
Broker for interacting with Llama. Refactors scheduler and coordinator
to move fragment-to-host assignment logic into scheduler. Local test
setup uses MiniLLama.
Change-Id: Ic7b0fe43de52d30f4207b4e65cce7e6a294e54e1
We weren't attaching resources to the row batch when starting a new
row group, so it was possible for string data to be overwritten. This
patch removes CloseStreams() and merges its functionality with
AttachCompletedResources() so it's not possible to destroy streams
without transferring the resources first. It also merges and removes
ScannerContext::Close().
Also adds test cases for IMPALA-720.
Change-Id: Ia8f40c7d39d8702716f1d337fe797e2696bd0fcb
parquet-mr had a bug where it didn't include the dictionary page's
header in the total column size. We now compensate for this by
detecting these files and padding the scan range length. This required
changing how the scanner detects when it's finished: it now counts the
number of rows rather than checking eosr (since the scan range may be
longer than the column).
Change-Id: Id9933808b965003c0c3b3aa78c32fe29a0c4bcbe
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1097
Reviewed-by: Skye Wanderman-Milne <skye@cloudera.com>
Tested-by: jenkins
The FE was creating class loaders with the HDFS locations of Hive UDF
libs, rather than the local locations created by the BE. Our tests
still passed since we only used UDFs already on the classpath
(e.g. Hive builtins).
Change-Id: Idbe9c98ad6adb84b70cb44efbf9ad0afc53366ca
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1081
Reviewed-by: Skye Wanderman-Milne <skye@cloudera.com>
Tested-by: jenkins
Updates our compute stats script to execute using Impala. This allows us
to easily compute stats on all tables in a database or all tables in the
metastore.
The updated stats caused one of the TPCH plans to change so this also
updates the TPCH planner test results.
Change-Id: I17e5dcd1036a35e40eb4eb2c8e4a20702db9049c
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1024
Reviewed-by: Lenni Kuff <lskuff@cloudera.com>
Tested-by: jenkins
With this change we now detect if a table is read-only and disable INSERT/LOAD operations
on these tables. A table is read-only if Impala does not have write permission on the HDFS
base directory of the table or any one of the partition directories (if
the table is partitioned).
Change-Id: I25515b2d0ffb7fe297359437fd937a3d6e0406a0
Reviewed-on: http://gerrit.ent.cloudera.com:8080/713
Reviewed-by: Lenni Kuff <lskuff@cloudera.com>
Tested-by: Lenni Kuff <lskuff@cloudera.com>
Before this, we had to specify the entire mangled symbol. This can be quite
long and quite tedious (take a look at some of the create UDA test cases that
specify all the symbols).
This patch adds some code to convert from the user function signature to the
mangled name. This means the user can specify the unmangled name and we can
do the symbol lookup. The mangling rules are pretty convoluted but if it is
messed up, the user can always specify the full symbol.
Some other minor cleanup in:
- JNI from FE to BE
- UDFs/UDAs that are loaded as test data
Change-Id: I733dbf3a72cb7b06221c27e622d161bcca0d74a8
Reviewed-on: http://gerrit.ent.cloudera.com:8080/624
Reviewed-by: Nong Li <nong@cloudera.com>
Tested-by: Nong Li <nong@cloudera.com>
This patch also adds a number of improvements to NativeUdfExpr. Highlights include:
* Correctly handling the lowering of AnyVal struct types (required for ABI compatibility)
* A rudimentary library cache for reusing handles produced by dlopen
* More complicated test cases
Change-Id: Iab9acdd7d7c4308e5d7ee3210f21b033fda5a195
Reviewed-on: http://gerrit.ent.cloudera.com:8080/540
Tested-by: jenkins
Reviewed-by: Skye Wanderman-Milne <skye@cloudera.com>
Tested-by: Skye Wanderman-Milne <skye@cloudera.com>
This changes adds support for SQL statement authorization in Impala. The authorization
works by updating the Catalog API to require a User + Privilege when getting Table/Db
objects (and in the future can be extended to cover columns as well).
If the user doesn't have permission to access the object, an AuthorizationException is
thrown. The authorization checks are done during analysis as new Catalog objects are
encountered.
These changes build on top of the Hive Access code which handles the actually
processing of authorization requests. The authorization is currently based
on a "policy file" which will be stored in HDFS. This policy file is read once
on startup and then reloaded every 5 minutes. It can also be reloaded on a
specific impalad by executing a "refresh" command.
Authorization is enabled by setting:
--server_name='server1'
and then pointing the impalad to the policy file using the flag:
--authorization_policy_file=/path/to/policy/file
any authorization configuration problems will result in impalad failing to
start.
Always reload region server info.
Clear keyRange.start/stopkey before setting it in setKeyRangeStart/End.
Split HBase tables into multiple regions.
I've to disable HBase scanrangelocations planner test because region assigment
is non-deterministic. I'll have a follow up patch to address that.
This change adds support for auxiliary worksloads, tests, and datasets. This is useful
to augment the regular test runs with some additional tests that do not belong in the
main Impala repo.
This works around a problem with computing table stats via the Hive Meta Store client
API. When executing these stements via the MetaStoreClient, all tables were getting a
num_rows=0 value returned from the ANALYZE TABLE query.
With this change the Python tests will now be called as part of buildall and
the corresponding Java tests have been disabled. The new tests can also be
invoked calling ./tests/run-tests.sh directly.
This includes a fix from Nong that caused wrong results for limit on non-io
manager formats.
This is the first set of changes required to start getting our functional test
infrastructure moved from JUnit to Python. After investigating a number of
option, I decided to go with a python test executor named py.test
(http://pytest.org/). It is very flexible, open source (MIT licensed), and will
enable us to do some cool things like parallel test execution.
As part of this change, we now use our "test vectors" for query test execution.
This will be very nice because it means if load the "core" dataset you know you
will be able to run the "core" query tests (specified by --exploration_strategy
when running the tests).
You will see that now each combination of table format + query exec options is
treated like an individual test case. this will make it much easier to debug
exactly where something failed.
These new tests can be run using the script at tests/run-tests.sh
This change includes a number of improvements for the test data loading framework:
* Named sections for schema template definitions
* Removal of uneeded sections from schema template definitions (ex. ANALYZE TABLE)
* More granular data loading via table name filters
* Improved robustness in detecting failed data loads
* Table level constraints for specific file formats
* Re-written compute stats script
