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
Re-order union operands descending by their estimated per-host memory,
s.t. parent nodes can gauge the peak memory consumption of a MergeNode after
opening it during execution (a MergeNode opens its first operand in Open()).
Scan nodes are always ordered last because they can dynamically scale down their
memory usage, whereas many other nodes cannot (e.g., joins, aggregations).
One goal is to decrease the likelihood of a SortNode parent claiming too much
memory in its Open(), possibly causing the mem limit to be hit when subsequent
union operands are executed.
Change-Id: Ia51caaffd55305ea3dbd2146cd55acc7da67f382
Reviewed-on: http://gerrit.ent.cloudera.com:8080/3146
Reviewed-by: Alex Behm <alex.behm@cloudera.com>
Tested-by: Alex Behm <alex.behm@cloudera.com>
Reviewed-on: http://gerrit.ent.cloudera.com:8080/3213
Tested-by: jenkins
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
Order-by without limit in the query statement corresponding an INSERT
or CTAS must be ignored because
i) There is no guarantee on row ordering when the target table is scanned again
i.e. 'select * from table' may return rows in any order, regardless of how the
rows were inserted, and
ii) Ignoring (and not flagging an error) is consistent with the treatment of
order-by w/o limit in nested queries, union operands etc.
Currently, an order-by w/o limit in a QueryStmt is only evaluated if the analyzer is
the root analyzer (has no ancestors).
However, a new child analyzer is not created for the QueryStmt in an InsertStmt, so this
technique fails for inserts. The correct thing to do is to use a child analyzer for that
QueryStmt, but this has spill-over scoping effects for analysis of with clauses.
This patch adds a flag, similar to the isExplain flag to the analyzer to identify
insert statements.
Change-Id: I9ded587cfea75eca0b7a43ee9b0df0a6c8ecb602
Reviewed-on: http://gerrit.ent.cloudera.com:8080/3044
Reviewed-by: Srinath Shankar <sshankar@cloudera.com>
Tested-by: jenkins
Reviewed-on: http://gerrit.ent.cloudera.com:8080/3060
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
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>
- added PlanNode.numNodes, PlanNode.avgRowSize and PlanNode.computeStats()
- fixing up some cardinality estimates
- Planner now tries to do a cost-based decision between broadcast join and join with full repartitioning (both inputs)
- ExchangeNode now distinguishes between its input and output row descriptor: the output potentially contains more tuples
- fixed problem related to cancellation and concurrent hash table builds.
Not included:
- partitioned joins that take advantage of existing partitions of the inputs; those will have to wait for a follow-on change
- 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
This change updates the run-benchmark script to enable it to target one or more
workloads. Now benchmarks can be run like:
./run-benchmark --workloads=hive-benchmark,tpch
We lookup the workload in the workloads directory, then read the associated
query .test files and start executing them.
To ensure the queries are not duplicated between benchmark and query tests, I
moved all existing queries (under fe/src/test/resources/* to the workloads
directory. You do NOT need to look through all the .test files, I've just moved
them. The one new file is the 'hive-benchmark.test' which contains the hive
benchmark queries.
Also added support for generating schema for different scale factors as well as
executing against these scale factors. For example, let's say we have a dataset
with a scale factor called "SF1". We would first generate the schema using:
./generate_schema_statements --workload=<workload> --scale_factor="SF3"
This will create tables with a unique names from the other scale factors.
Run the generated .sql file to load the data. Alternatively, the data can loaded
by running a new python script:
./bin/load-data.py -w <workload1>,<workload2> -e <exploration strategy> -s [scale factor]
For example: load-data.sh -w tpch -e core -s SF3
Then run against this:
./run-benchmark --workloads=<workload> --scale_factor=SF3
This changeset also includes a few other minor tweaks to some of the test
scripts.
Change-Id: Ife8a8d91567d75c9612be37bec96c1e7780f50d6
