This change updates our DDL syntax support to allow for using 'STORED AS PARQUET'
as well as 'STORED AS PARQUETFILE'. Moving forward we should prefer the new syntax,
but continue to support the old. I made the same change for 'AVROFILE', but since
we have not yet documented the 'AVROFILE' syntax I left out support for the old syntax.
Change-Id: I10c73a71a94ee488c9ae205485777b58ab8957c9
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1053
Reviewed-by: Marcel Kornacker <marcel@cloudera.com>
Tested-by: jenkins
The parquet file stores the application version that wrote it so
is different between our c4 and c5 branches.
HBase storage is also not guaranteed to be identical across versions.
Change-Id: I02984a55e0678756e50c1fff6db22c43788d3916
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1028
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: jenkins
A compute stats command computes the table and column stats for a given
table and persists them in the metastore.
The table stats consist of the per-partition and per-table row count.
The column stats are computed on a per-table basis and consist of the
number of distinct values and the number of NULLs per column.
This patch introduces a new 'child query' concept that
compute stats utilizes. Child queries are cancelled
if the parent query is cancelled. A compute stats stmt is
executed by the following query hirarchy:
parent: compute stats query (DDL)
- child: compute table stats query (QUERY)
- child: compute column stats query (QUERY)
The new child query concept is necessary to decouple child query fetches
from parent query fetches, i.e., we could not execute a child query as
part of the original compute stats query, because then a client could
fetch the results we need for updating the Metastore statistics. The
reason why our existing CTAS works without this decoupling
is that its insert 'child query' is not fetchable.
Change-Id: I560533e3cb09bcbbdb3eea7fcf0b460bc6b36dcd
Reviewed-on: http://gerrit.ent.cloudera.com:8080/873
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: jenkins
This is implemented in the BE using HLL (but we could change this in the
future).
These estimates usually work better than the other algorithm we have and
we've not implemented all the improvements from the google paper.
Change-Id: Ied715ddd0e1a7cbe7f5f90469f1ed3d4b9c537c7
Reviewed-on: http://gerrit.ent.cloudera.com:8080/956
Reviewed-by: Nong Li <nong@cloudera.com>
Tested-by: jenkins
Adds support for skipping a number of rows with an ORDER BY clause and a LIMIT. Hive
does not support OFFSET so creating a view with an OFFSET will not work in Hive.
For example, "SELECT * FROM T1 ORDER BY ID LIMIT 20 OFFSET 5" will do the sorting, skip
5 rows, then return the next 20. OFFSET requires an ORDER BY clause.
Note this is not very efficient as we must actually keep (limit+offset) rows in memory
in the topn-node, and all child sort nodes must as well. Users should be careful when
using this feature.
Change-Id: I4d7021c278296e7bdbfa0e6f2699cd6f23eef59d
Reviewed-on: http://gerrit.ent.cloudera.com:8080/900
Tested-by: jenkins
Reviewed-by: Matthew Jacobs <mj@cloudera.com>
Tested-by: Matthew Jacobs <mj@cloudera.com>
We were previously wasting memory by always reading into 8MB IO
buffers, even when the data read was much less than 8MB. With this
patch, the IO manager picks a buffer size closer to the actual amount
being read (we don't use the exact size so we can continue to recycle
buffers). The minimum IO buffer size is determined via the
--min_buffer_size flag, and the max IO buffer size via the --read_size
flag.
This technique also helps with IMPALA-652, since short columns will
not use as much memory as before (we will not use considerably more
memory than the size of the table).
This patch also changes StringBuffer to use a doubling strategy so it
doesn't end up allocating many large unused buffers, and has the
scanner context use the requested length as the sync read size if it's
larger than the size produced by read_past_size_cb(). These changes
help prevent the boundary buffer in the scanner context from
allocating excess memory.
Change-Id: I0efb3b023ddfddb08bca22d5cb5f9511fb4d6c50
Reviewed-on: http://gerrit.ent.cloudera.com:8080/938
Reviewed-by: Skye Wanderman-Milne <skye@cloudera.com>
Tested-by: jenkins
When dropping functions, we neeed to remove the function from the list
of Functions with that name AND remove the list from the Function map if
the list is empty. The second part wasn't happening.
Also fixes the test_ddl to properly create all test databases.
Change-Id: Id85af7d5db74a31161f48bea3816bdf734063133
Reviewed-on: http://gerrit.ent.cloudera.com:8080/952
Reviewed-by: Nong Li <nong@cloudera.com>
Tested-by: jenkins
This change adds support for cluster-synchronized catalog operations. This provides the
guaranteethat after a catalog op completes, all other subscribers to the catalog topic have
also processed that update. This is useful when load balancing, because a common workflow
is to target a different impalad for each statement executed.
For example if each of the following were executed sequentially, but targeting
a different node:
1) CREATE TABLE Foo
2) INSERT INTO Foo
3) SELECT * FROM Foo
4) INSERT INTO Foo ....
Since both the INSERT and the CREATE update the catalog, it would not work as expected
without this patch. The user might either get a "table not found" error or would be
missing partition information from the INSERT.
The downside is that this approach to DDL takes a bit longer because we need to wait
until all subscribers have processed an update. If all nodes are healthy, this overhead
should not be significantly longer than the current DDL time. However, a single bad node
might slow down or completely block the completion of all DDL operations. By default
this feature is disabled, but it can be enabled using a new query option: SYNCED_DDL=1
To test this, the base test suite was updated to support selecting a random impalad
to execute each query section in a query test file. This is currently only enabled
for the insert and DDL tests, but could be leveraged by more tests in the future.
TODO: Add additional failure tests around this functionality.
TODO: Add an explicit "sync" statement so users do not need to run all their DDL
in this mode (since it is slower).
Change-Id: I45e757a931bf2a4740cc0cdd1e76ce49a1e22b83
Reviewed-on: http://gerrit.ent.cloudera.com:8080/899
Reviewed-by: Ishaan Joshi <ishaan@cloudera.com>
Tested-by: jenkins
This patch fixes an issue where Impala would crash if two partitions had
the same HDFS location. This is now fixed in hdfs-scan-node. It also includes some
cleanup and bug fixes to the FE partition related classes and adds tests.
There is still a problem where partition location metadata is not sent
to the BE for INSERT statements, but that will be resolved in a separate
patch.
Change-Id: I0f1c3113d654f7d2b410f00e793ff6b0cae1ae18
Reviewed-on: http://gerrit.ent.cloudera.com:8080/876
Reviewed-by: Alan Choi <alan@cloudera.com>
Tested-by: jenkins
Adds support for "show create table", a DDL statement that outputs a DDL statement that
creates the specified table.
In general, the output DDL works in Impala, so a user can copy the output and execute it
to create the same table. However, there are a few special cases that output Hive DDL
because we do not support creating some tables in Impala: HBase tables and tables with
LZO compressed text. When we do support creating these tables in Impala, users should
be able to execute the DDL in Impala as well.
Change-Id: I8c130297a657810dea5b994bf99d72b0e61b847b
Reviewed-on: http://gerrit.ent.cloudera.com:8080/842
Reviewed-by: Matthew Jacobs <mj@cloudera.com>
Tested-by: Matthew Jacobs <mj@cloudera.com>
Fixed the following stats-related bugs:
- Per-partition row count was not distributed properly via CatalogService
- HBase column stats were not loaded and distributed properly
Enhancements to test framework:
- Allow regex specification of expected row or column values
- Fixed expected results of some tests because the test framework
did not catch that they were incorrect
Change-Id: I1fa8e710bbcf0ddb62b961fdd26ecd9ce7b75d51
Reviewed-on: http://gerrit.ent.cloudera.com:8080/813
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: jenkins
This change modifies that behavior of NULL ordering such that nulls always
compare greater than other values, but "nulls first" or "nulls last" can be used
to explicitly specify if nulls should be sorted first or last regardless of the
asc/desc.
Change-Id: I92feda1e7f42249de4009afd39f8395a0a32a2f8
Reviewed-on: http://gerrit.ent.cloudera.com:8080/812
Reviewed-by: Marcel Kornacker <marcel@cloudera.com>
Reviewed-by: Matthew Jacobs <mj@cloudera.com>
Tested-by: Matthew Jacobs <mj@cloudera.com>
This patch refactors HDFSScanNode to copy and prepare all conjunct
exprs in Prepare(), rather than in the scanner threads. This is
necessary so the UDF exprs get codegen'd. Prepare() also only codegens
the functions for the necessary file formats now, rather than for all
file formats regardless of what's actually be scanned.
Change-Id: Ic3220cbd0cba9a3baa138b1f50ecdc6889ed0cd1
Reviewed-on: http://gerrit.ent.cloudera.com:8080/710
Reviewed-by: Skye Wanderman-Milne <skye@cloudera.com>
Tested-by: Skye Wanderman-Milne <skye@cloudera.com>
Exprs used for partition pruning are prepared/evaluated with a
separate RuntimeState. If these exprs use UDFs, the runtime state
needs access to the process's ExecEnv so we can use the LibCache and
the IR produced by the UDF exprs needs to be optimized and jit'd.
Change-Id: If7c1d6ebc0015ef3c21a0421c1a36cad4be66625
Reviewed-on: http://gerrit.ent.cloudera.com:8080/695
Tested-by: jenkins
Reviewed-by: Skye Wanderman-Milne <skye@cloudera.com>
Tested-by: Skye Wanderman-Milne <skye@cloudera.com>
Unfortunately, the BE does not have the codegen path to execute UDAs.
This puts some restrictions on the UDAs we can run.
- No IR UDAs
- No varargs
- Must have 8 arguments or less.
The code to do this is almost all there for UDFs but I'm not sure I'll get to it.
Change-Id: I8a06e635a9138397c8474a5704c3e588bb92347b
Reviewed-on: http://gerrit.ent.cloudera.com:8080/703
Reviewed-by: Nong Li <nong@cloudera.com>
Tested-by: Nong Li <nong@cloudera.com>
AnalyzeDDLTest was failing because the fesupport binary couldn't
resolve a function used in libTestUdfs.so (the function was defined in
udf.cc, rather than udf.h). I couldn't figure out how to cleanly build
udf.cc into the libTestUdfs.so, so instead I removed the use of the
function in test-udfs.cc.
Change-Id: I81243547584a5b49a5f9265d0d17e035e18d6110
Reviewed-on: http://gerrit.ent.cloudera.com:8080/694
Reviewed-by: Skye Wanderman-Milne <skye@cloudera.com>
Tested-by: Skye Wanderman-Milne <skye@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 redoes how the aggregation node is implemented. The functionality is
now split between aggregation-node, agg-expr and aggregate-functions. This is a working
progress (there's still a lot of debug stuff I added that needs to be cleaned up) but
it does pass the tests.
Aggregation-node is now very simple and now only deals with the grouping part.
Aggregate-expr serves as the glue between the agg node and the aggregate functions.
The aggregation functions are implemented with the UDA interface. I've reimplemented
our existing aggregate functions with this setup. For true UDAs, the binaries would be
loaded in aggregate-expr.
This also includes some preliminary changes in the FE. We now need to annotate each
AggNode as executing the update vs. merge phase (root aggs execute update, others
execute merge) and if it needs a finalize step (only the root does). This is more
general than our builtins which are too simple to need this structure.
There is a big TODO here to allow the intermediate types between agg nodes to change.
For example, in distinct estimate, the input type is the column type and the output type
is a bigint. We'd like the intermediate type to be CHAR(256). This is different since
currently, the intermediate type and output type have always been the same. We've hacked
around this by having both the intermediate and output type be TYPE_STRING. I've left
this for another patch (changing the BE to support this is trivial).
For aggregates that result in strings, we used to store some additional stuff past the
end of the tuple. The layout was:
<tuple> <length of 1st string buffer>,<length of 2nd string buffer>, etc
The rationale for this is that we want to reuse the buffer for min/max and grow the buffer
more quickly for group_concat. This breaks down the abstraction between agg-expr and
agg-node and is not something UDAs can use in general. Rather than try to hack around
this, I think the proper solution is to the intermediate type not be StringValue and
to contain the buffer length itself.
This patch also resurrects the distinct estimate code. The distinct estimate functions
exercise all of the code paths.
Change-Id: Ic152a2cd03bc1713967673681e1e6204dcd80346
Reviewed-on: http://gerrit.ent.cloudera.com:8080/564
Reviewed-by: Nong Li <nong@cloudera.com>
Tested-by: Nong Li <nong@cloudera.com>
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>
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>
I looked around some and I think having create/drop/show [aggregate] function
seems reasonable and extends nicely for UDTs.
The create aggregate function can accept a lot of arguments. The non-essential one, I
went with resolving them by name rather than position (i.e. argName="value"). I think
this is better for the user than specifying it by position.
The grammar is:
CREATE AGGREGATE <name>(<arg_types>) RETURNS <type> [INTERMEDIATE <type>]
LOCATION '/path' UpdateFn='Fn' [comment='comment']
[SerializeFn='symbol'] [MergeFn='symbol'] [InitFn='symbol'] [FinalizeFn='symbol']
The optional args at the end can be in any order. If the other symbols are not
specified, we derive them from the UpdateFn symbol that's required. The analyzer
would try to figure it out and fail if we can't find the derived symbol in the binary.
The simplest example would be:
CREATE AGGREGATE FUNCTION count(float) RETURNS BIGINT LOCATION '/path'
UpdateFn='CountUpdateFn';
In which case we assume the intermediate type is the return type and the other functions
are called 'CountInitFn', 'CountSerializeFn', 'CountMergeFn' 'CountFinalizeFn'.
Change-Id: Iefc5741293050f5b295df28e9d1a7d039ead8675
Reviewed-on: http://gerrit.ent.cloudera.com:8080/513
Reviewed-by: Nong Li <nong@cloudera.com>
Tested-by: Nong Li <nong@cloudera.com>
Doing it this way makes sure we don't bail early on the Close path
which is rarely the right thing to do. This found a few places where
we were not doing proper cleanup because of this.
Change-Id: Ie663c68398c14589b5cbc1bd980644b0b10fd865
Reviewed-on: http://gerrit.ent.cloudera.com:8080/373
Reviewed-by: Nong Li <nong@cloudera.com>
Tested-by: Nong Li <nong@cloudera.com>
Changes MemLimit to MemTracker:
- the limit is optional
- it also records a label and an optional parent
- Consume() and Release() also update the ancestors and there's also a new
AnyLimitExceeded(), which also checks the ancestors
- the consumption counter is a HighwaterMarkCounter and can optionally be created
as part of a profile
Each fragment instance now has a MemTracker that is part of a 3-level
hierarchy: process, query, fragment instance.
Change-Id: I5f580f4956fdf07d70bd9a6531032439aaf0fd07
Reviewed-on: http://gerrit.ent.cloudera.com:8080/339
Reviewed-by: Nong Li <nong@cloudera.com>
Tested-by: Nong Li <nong@cloudera.com>
