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
This patch changes the planning of a UnionStmt s.t. it always produces a single fragment
with a MergeNode connecting all child fragments as its root.
The data partition of the returned fragment and how the child fragments are merged
depends on the data partitions of the child fragments:
- All child fragments are unpartitioned or partitioned: The returned fragment is
has a UNPARTITIONED or RANDOM data partition, respectively. The MergeNode absorbs
the plan trees of all child fragments.
- Mixed partitioned/unpartitioned child fragments: The returned fragment is
RANDOM partitioned. The plan trees of all partitioned child fragments are absorbed
into the MergeNode. All unpartitioned child fragments are connected to the
MergeNode via a RANDOM exchange, and remain unchanged otherwise.
Also adds support for random partitioned data exchanges.
Change-Id: I82b2d12c104d98c4e7133234653ee1b67658ef7a
Reviewed-on: http://gerrit.ent.cloudera.com:8080/2876
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: jenkins
Reviewed-on: http://gerrit.ent.cloudera.com:8080/3143
The runtime profile as we present it is not very useful and I think the structure of
it makes it hard to consume. This patch adds a new client facing schemed set of
counters that are collected from the runtime profiles. For example, with this structure
it would be easy to have the shell get the stats of a running query and print a useful
progress report or to check the most relevant metrics for diagnosing issues.
Here's an example of the output for one of the tpch queries:
Operator #Hosts Avg Time Max Time #Rows Est. #Rows Peak Mem Est. Peak Mem Detail
------------------------------------------------------------------------------------------------------------------------
09:MERGING-EXCHANGE 1 79.738us 79.738us 5 5 0 -1.00 B UNPARTITIONED
05:TOP-N 3 84.693us 88.810us 5 5 12.00 KB 120.00 B
04:AGGREGATE 3 5.263ms 6.432ms 5 5 44.00 KB 10.00 MB MERGE FINALIZE
08:AGGREGATE 3 16.659ms 27.444ms 52.52K 600.12K 3.20 MB 15.11 MB MERGE
07:EXCHANGE 3 2.644ms 5.1ms 52.52K 600.12K 0 0 HASH(o_orderpriority)
03:AGGREGATE 3 342.913ms 966.291ms 52.52K 600.12K 10.80 MB 15.11 MB
02:HASH JOIN 3 2s165ms 2s171ms 144.87K 600.12K 13.63 MB 941.01 KB INNER JOIN, BROADCAST
|--06:EXCHANGE 3 8.296ms 8.692ms 57.22K 15.00K 0 0 BROADCAST
| 01:SCAN HDFS 2 1s412ms 1s978ms 57.22K 15.00K 24.21 MB 176.00 MB tpch.orders o
00:SCAN HDFS 3 8s032ms 8s558ms 3.79M 600.12K 32.29 MB 264.00 MB tpch.lineitem l
Change-Id: Iaad4b9dd577c375006313f19442bee6d3e27246a
Reviewed-on: http://gerrit.ent.cloudera.com:8080/2964
Reviewed-by: Nong Li <nong@cloudera.com>
Tested-by: jenkins
Enable order-by without limit
Added BufferedBlockMgr to allocate buffers and spill to disk.
Added Sorter for the external sort impelementation
Added new SortNode execution node that completely sorts its input
Changes to enable writing in IoMgr went in a separate patch.
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1539
Reviewed-by: Srinath Shankar <sshankar@cloudera.com>
Tested-by: jenkins
Conflicts:
testdata/workloads/functional-planner/queries/PlannerTest/tpcds-all.test
Change-Id: I3ece32affe5b006f53bbdfcc03ded01471e818ac
Reviewed-on: http://gerrit.ent.cloudera.com:8080/2900
Reviewed-by: Srinath Shankar <sshankar@cloudera.com>
Tested-by: jenkins
The issue: Computing the full transitive closure for all slots can be very
expensive (10s of seconds for >2k slots, minutes for >4k slots).
Queries with many views and/or unions were affected most because each
union/view adds a new tuple with slots, increasing the total number of slots.
The fix: The new algorithm exploits the sparse structure of the value transfer
graph for a significant speedup (>100x). The high-level steps are:
1. Identify complete subgraps based on bi-directional value transfers, and
coalesce the slots of each complete subgraph into a single slot.
2. Map the remaining uni-directional value transfers into the new slot domain.
3. Identify the connected components of the uni-directional value transfers.
This step partitions the value transfers into disjoint sets.
4. Compute the transitive closure of each partition from (3) in the new slot
domain separately. Hopefully, the partitions are small enough to afford
the O(N^3) complexity of the brute-force transitive closure computation.
Change-Id: I35b57295d8f04b92f00ac48c04d1ef1be4daf41b
Reviewed-on: http://gerrit.ent.cloudera.com:8080/2360
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: jenkins
This patch cleans up analysis and execution of scalar and aggregate functions
so that there is no difference between how builtins and user functions are
handled. The only difference is that the catalog is populated with the builtins
all the time.
The BE always gets a TFunction object and just executes it (builtins will have
an empty hdfs file location).
This removes the opcode registry and all of the functionality is subsumed by
the catalog, most of which was already duplicated there anyway.
This also introduces the concept of a system database; databases that the
user cannot modify and is populated automatically on startup.
Change-Id: Iaa3f84dad0a1a57691f5c7d8df7305faf01d70ed
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1386
Reviewed-by: Nong Li <nong@cloudera.com>
Tested-by: jenkins
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1577
There are now 4 explain levels summarized as follows:
- Level 0: MINIMAL
Non-fragmented parallel plan only showing plan nodes with minimal attributes
- Level 1: STANDARD
Non-fragmented parallel plan with some details in plan nodes
- Level 2: EXTENDED
Non-fragmented parallel plan with full details in plan nodes including
the table/column stats, row size, #hosts, cardinality,
and estimated per-host memory requirement
- Level 3: VERBOSE
Fragmented parallel plan with full details (like level 2)
This patch also includes several bugfixes related to plan costing and/or
testing of explain plans.
Change-Id: I622310f01d1b3d53ea1031adaf3b3ffdd94eba30
Reviewed-on: http://gerrit.ent.cloudera.com:8080/1211
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: jenkins
Introduces STRAIGHT_JOIN keyword to prevent join order optimization.
Structural changes to the planning framework:
- slot materialization: the decision whether to materialize a slot now happens *prior* to
plan generation. This is needed in order to be able to generate accurate cost estimates
at plan generation time. see QueryStmt.materializeRequiredSlots()
- added PlanNode.init(), which initializes the entire state of a PlanNode; this subsumes
finalize()
* computeMemLayout() now happens per-tuple in the corresponding ScanNode's init()
* init() calls computeStats() by default; also marks slots as materialized and calls
TupleDescriptor.computeMemLayout()
- added PlanNode.tblRefIds_
- restructured UnionStmt and union plan generation to fit pred propagation model:
all tuples are created (and equiv predicates registered) prior to plan generation
- added Expr.isAuxExpr
Change-Id: I475c1645bfca9e84ae6e5f529e7781d9532e5c9a
Reviewed-on: http://gerrit.ent.cloudera.com:8080/955
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Reviewed-by: Lenni Kuff <lskuff@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>
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>
- this adds a SelectNode that evaluates conjuncts and enforces the limit
- all limits are now distributed: enforced both by the child plan fragment and
by the merging ExchangeNode
- all limits w/ Order By are now distributed: enforced both by the child plan fragment and
by the merging TopN node
