[
https://issues.apache.org/jira/browse/PIG-4871?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
liyunzhang_intel updated PIG-4871:
----------------------------------
Description:
In current code base, we use OperatorPlan#forceConnect() while merge the
physical plan of spliter and splittee in MultiQueryOptimizationSpark.
The difference between OperatorPlan#connect and OperatorPlan#forceConnect is
not checking whether support multiOutputs and multiInputs or not in
forceConnect.
{code}
/**
* connect from and to and ignore some judgements: like ignoring judge
whether from operator supports multiOutputs
* and whether to operator supports multiInputs
*
* @param from Operator data will flow from.
* @param to Operator data will flow to.
* @throws PlanException if connect from or to which is not in the plan
*/
public void forceConnect(E from, E to) throws PlanException {
markDirty();
// Check that both nodes are in the plan.
checkInPlan(from);
checkInPlan(to);
mFromEdges.put(from, to);
mToEdges.put(to, from);
}
{code}
Let's use an example to explain why add forceConnect before.
{code}
A = load './split5' AS (a0:int, a1:int, a2:int);
B = foreach A generate a0, a1;
C = join A by a0, B by a0;
D = filter C by A::a1>=B::a1;
store D into './split5.out';
{code}
before multiquery optimization
{code}
scope-37->scope-43
scope-43
#--------------------------------------------------
# Spark Plan
#--------------------------------------------------
Spark node scope-37
Store(hdfs://zly2.sh.intel.com:8020/tmp/temp-535495592/tmp-2029463812:org.apache.pig.impl.io.InterStorage)
- scope-38
|
|---A: New For Each(false,false,false)[bag] - scope-10
| |
| Cast[int] - scope-2
| |
| |---Project[bytearray][0] - scope-1
| |
| Cast[int] - scope-5
| |
| |---Project[bytearray][1] - scope-4
| |
| Cast[int] - scope-8
| |
| |---Project[bytearray][2] - scope-7
|
|---A:
Load(hdfs://zly2.sh.intel.com:8020/user/root/split5:org.apache.pig.builtin.PigStorage)
- scope-0--------
Spark node scope-43
D: Store(fakefile:org.apache.pig.builtin.PigStorage) - scope-36
|
|---D: Filter[bag] - scope-32
| |
| Greater Than or Equal[boolean] - scope-35
| |
| |---Project[int][1] - scope-33
| |
| |---Project[int][4] - scope-34
|
|---C: New For Each(true,true)[tuple] - scope-31
| |
| Project[bag][1] - scope-29
| |
| Project[bag][2] - scope-30
|
|---C: Package(Packager)[tuple]{int} - scope-24
|
|---C: Global Rearrange[tuple] - scope-23
|
|---C: Local Rearrange[tuple]{int}(false) - scope-25
| | |
| | Project[int][0] - scope-26
| |
|
|---Load(hdfs://zly2.sh.intel.com:8020/tmp/temp-535495592/tmp-2029463812:org.apache.pig.impl.io.InterStorage)
- scope-39
|
|---C: Local Rearrange[tuple]{int}(false) - scope-27
| |
| Project[int][0] - scope-28
|
|---B: New For Each(false,false)[bag] - scope-20
| |
| Project[int][0] - scope-16
| |
| Project[int][1] - scope-18
|
|---Load(hdfs://zly2.sh.intel.com:8020/tmp/temp-535495592/tmp-2029463812:org.apache.pig.impl.io.InterStorage)
- scope-41--------{code}
after multiquery optimization
{code}
after multiquery optimization:
scope-37
#--------------------------------------------------
# Spark Plan
#--------------------------------------------------
Spark node scope-37
D: Store(fakefile:org.apache.pig.builtin.PigStorage) - scope-36
|
|---D: Filter[bag] - scope-32
| |
| Greater Than or Equal[boolean] - scope-35
| |
| |---Project[int][1] - scope-33
| |
| |---Project[int][4] - scope-34
|
|---C: New For Each(true,true)[tuple] - scope-31
| |
| Project[bag][1] - scope-29
| |
| Project[bag][2] - scope-30
|
|---C: Package(Packager)[tuple]{int} - scope-24
|
|---C: Global Rearrange[tuple] - scope-23
|
|---C: Local Rearrange[tuple]{int}(false) - scope-25
| | |
| | Project[int][0] - scope-26
| |
| |---A: New For Each(false,false,false)[bag] - scope-10
| | |
| | Cast[int] - scope-2
| | |
| | |---Project[bytearray][0] - scope-1
| | |
| | Cast[int] - scope-5
| | |
| | |---Project[bytearray][1] - scope-4
| | |
| | Cast[int] - scope-8
| | |
| | |---Project[bytearray][2] - scope-7
| |
| |---A:
Load(hdfs://zly2.sh.intel.com:8020/user/root/split5:org.apache.pig.builtin.PigStorage)
- scope-0
|
|---C: Local Rearrange[tuple]{int}(false) - scope-27
| |
| Project[int][0] - scope-28
|
|---B: New For Each(false,false)[bag] - scope-20
| |
| Project[int][0] - scope-16
| |
| Project[int][1] - scope-18
|
|---A: New For Each(false,false,false)[bag] - scope-10
| |
| Cast[int] - scope-2
| |
| |---Project[bytearray][0] - scope-1
| |
| Cast[int] - scope-5
| |
| |---Project[bytearray][1] - scope-4
| |
| Cast[int] - scope-8
| |
| |---Project[bytearray][2] - scope-7
|
|---A:
Load(hdfs://zly2.sh.intel.com:8020/user/root/split5:org.apache.pig.builtin.PigStorage)
- scope-0--------
{code}
We connect ForEach(scope-10) in SparkNode(scope-37) with ForEach(scope-20) and
LocalRearrange(scope-25) in SparkNode(scope-43). The successors of
ForEach(scope-10) are scope-20 and scope-25 after multiquery optimization. Here
we need use OperatorPlan#forceConnect(from, to) because
POForEach#supportsMultipleOutputs are false. *Why there is no problem in mr
mode?* in mr, clone ForEach(scope-10) as ForEach(scope-xxx), so the size of
successors of POForEach is always 1.
after multiquery optimization in mr mode
{code}
#--------------------------------------------------
# Map Reduce Plan
#--------------------------------------------------
MapReduce node scope-43
Map Plan
Union[tuple] - scope-44
|
|---C: Local Rearrange[tuple]{int}(false) - scope-25
| | |
| | Project[int][0] - scope-26
| |
| |---A: New For Each(false,false,false)[bag] - scope-53
| | |
| | Cast[int] - scope-48
| | |
| | |---Project[bytearray][0] - scope-47
| | |
| | Cast[int] - scope-50
| | |
| | |---Project[bytearray][1] - scope-49
| | |
| | Cast[int] - scope-52
| | |
| | |---Project[bytearray][2] - scope-51
| |
| |---A:
Load(hdfs://zly2.sh.intel.com:8020/user/root/split5:org.apache.pig.builtin.PigStorage)
- scope-46
|
|---C: Local Rearrange[tuple]{int}(false) - scope-27
| |
| Project[int][0] - scope-28
|
|---B: New For Each(false,false)[bag] - scope-20
| |
| Project[int][0] - scope-16
| |
| Project[int][1] - scope-18
|
|---A: New For Each(false,false,false)[bag] - scope-61
| |
| Cast[int] - scope-56
| |
| |---Project[bytearray][0] - scope-55
| |
| Cast[int] - scope-58
| |
| |---Project[bytearray][1] - scope-57
| |
| Cast[int] - scope-60
| |
| |---Project[bytearray][2] - scope-59
|
|---A:
Load(hdfs://zly2.sh.intel.com:8020/user/root/split5:org.apache.pig.builtin.PigStorage)
- scope-54--------
Reduce Plan
D: Store(fakefile:org.apache.pig.builtin.PigStorage) - scope-36
|
|---D: Filter[bag] - scope-32
| |
| Greater Than or Equal[boolean] - scope-35
| |
| |---Project[int][1] - scope-33
| |
| |---Project[int][4] - scope-34
|
|---C: Package(JoinPackager(true,true))[tuple]{int} - scope-24--------
Global sort: false
----------------
{code}
was:
In current code base, we use OperatorPlan#forceConnect() while merge the
physical plan of spliter and splittee in MultiQueryOptimizationSpark.
The difference between OperatorPlan#connect and OperatorPlan#forceConnect is
not checking whether support multiOutputs and multiInputs or not in
forceConnect.
{code}
/**
* connect from and to and ignore some judgements: like ignoring judge
whether from operator supports multiOutputs
* and whether to operator supports multiInputs
*
* @param from Operator data will flow from.
* @param to Operator data will flow to.
* @throws PlanException if connect from or to which is not in the plan
*/
public void forceConnect(E from, E to) throws PlanException {
markDirty();
// Check that both nodes are in the plan.
checkInPlan(from);
checkInPlan(to);
mFromEdges.put(from, to);
mToEdges.put(to, from);
}
{code}
Let's use an example to explain why add forceConnect before.
{code}
before multiquery optimization:
scope-102->scope-108
scope-108
#--------------------------------------------------
# Spark Plan
#--------------------------------------------------
Spark node scope-102
Store(hdfs://zly1.sh.intel.com:8020/tmp/temp1293488480/tmp128312889:org.apache.pig.impl.io.InterStorage)
- scope-103
|
|---A: New For Each(false,false,false)[bag] - scope-75
| |
| Cast[int] - scope-67
| |
| |---Project[bytearray][0] - scope-66
| |
| Cast[int] - scope-70
| |
| |---Project[bytearray][1] - scope-69
| |
| Cast[int] - scope-73
| |
| |---Project[bytearray][2] - scope-72
|
|---A:
Load(hdfs://zly1.sh.intel.com:8020/user/root/split5:org.apache.pig.builtin.PigStorage)
- scope-65--------
Spark node scope-108
D:
Store(hdfs://zly1.sh.intel.com:8020/user/root/split5.out:org.apache.pig.builtin.PigStorage)
- scope-101
|
|---D: Filter[bag] - scope-97
| |
| Greater Than or Equal[boolean] - scope-100
| |
| |---Project[int][1] - scope-98
| |
| |---Project[int][4] - scope-99
|
|---C: New For Each(true,true)[tuple] - scope-96
| |
| Project[bag][1] - scope-94
| |
| Project[bag][2] - scope-95
|
|---C: Package(Packager)[tuple]{int} - scope-89
|
|---C: Global Rearrange[tuple] - scope-88
|
|---C: Local Rearrange[tuple]{int}(false) - scope-90
| | |
| | Project[int][0] - scope-91
| |
|
|---Load(hdfs://zly1.sh.intel.com:8020/tmp/temp1293488480/tmp128312889:org.apache.pig.impl.io.InterStorage)
- scope-104
|
|---C: Local Rearrange[tuple]{int}(false) - scope-92
| |
| Project[int][0] - scope-93
|
|---B: New For Each(false,false)[bag] - scope-85
| |
| Project[int][0] - scope-81
| |
| Project[int][1] - scope-83
|
|---Load(hdfs://zly1.sh.intel.com:8020/tmp/temp1293488480/tmp128312889:org.apache.pig.impl.io.InterStorage)
- scope-106--------
after multiquery optimization:
scope-108
#--------------------------------------------------
# Spark Plan
#--------------------------------------------------
Spark node scope-108
D:
Store(hdfs://zly1.sh.intel.com:8020/user/root/split5.out:org.apache.pig.builtin.PigStorage)
- scope-101
|
|---D: Filter[bag] - scope-97
| |
| Greater Than or Equal[boolean] - scope-100
| |
| |---Project[int][1] - scope-98
| |
| |---Project[int][4] - scope-99
|
|---C: New For Each(true,true)[tuple] - scope-96
| |
| Project[bag][1] - scope-94
| |
| Project[bag][2] - scope-95
|
|---C: Package(Packager)[tuple]{int} - scope-89
|
|---C: Global Rearrange[tuple] - scope-88
|
|---C: Local Rearrange[tuple]{int}(false) - scope-90
| | |
| | Project[int][0] - scope-91
| |
| |---A: New For Each(false,false,false)[bag] - scope-119
| | |
| | Cast[int] - scope-114
| | |
| | |---Project[bytearray][0] - scope-113
| | |
| | Cast[int] - scope-116
| | |
| | |---Project[bytearray][1] - scope-115
| | |
| | Cast[int] - scope-118
| | |
| | |---Project[bytearray][2] - scope-117
| |
| |---A:
Load(hdfs://zly1.sh.intel.com:8020/user/root/split5:org.apache.pig.builtin.PigStorage)
- scope-112
|
|---C: Local Rearrange[tuple]{int}(false) - scope-92
| |
| Project[int][0] - scope-93
|
|---B: New For Each(false,false)[bag] - scope-85
| |
| Project[int][0] - scope-81
| |
| Project[int][1] - scope-83
|
|---A: New For Each(false,false,false)[bag] - scope-128
| |
| Cast[int] - scope-123
| |
| |---Project[bytearray][0] - scope-122
| |
| Cast[int] - scope-125
| |
| |---Project[bytearray][1] - scope-124
| |
| Cast[int] - scope-127
| |
| |---Project[bytearray][2] - scope-126
|
|---A:
Load(hdfs://zly1.sh.intel.com:8020/user/root/split5:org.apache.pig.builtin.PigStorage)
- scope-121--------
{code}
we just copy subplan(Load:scope-121) of SparkOperator(scope-102) to
SparkOperator(scope-108), connect POLoad(scope-121) with POForEach(scope-119)
and POForEach(scope-128). Here POLoad supports multiOutputs. we can use
OperatorPlan#connect. If a physical operator does not support multiOutputs, we
have to use OperatorPlan#forceConnect to do the connection. Now we can copy
the subplan and generate a new subplan and merge the subplan to the final plan
to do multiquery optimization like following to solve this problem.
{code}
scope-108
#--------------------------------------------------
# Spark Plan
#--------------------------------------------------
Spark node scope-108
D:
Store(hdfs://zly1.sh.intel.com:8020/user/root/split5.out:org.apache.pig.builtin.PigStorage)
- scope-101
|
|---D: Filter[bag] - scope-97
| |
| Greater Than or Equal[boolean] - scope-100
| |
| |---Project[int][1] - scope-98
| |
| |---Project[int][4] - scope-99
|
|---C: New For Each(true,true)[tuple] - scope-96
| |
| Project[bag][1] - scope-94
| |
| Project[bag][2] - scope-95
|
|---C: Package(Packager)[tuple]{int} - scope-89
|
|---C: Global Rearrange[tuple] - scope-88
|
|---C: Local Rearrange[tuple]{int}(false) - scope-90
| | |
| | Project[int][0] - scope-91
| |
| |---A: New For Each(false,false,false)[bag] - scope-119
| | |
| | Cast[int] - scope-114
| | |
| | |---Project[bytearray][0] - scope-113
| | |
| | Cast[int] - scope-116
| | |
| | |---Project[bytearray][1] - scope-115
| | |
| | Cast[int] - scope-118
| | |
| | |---Project[bytearray][2] - scope-117
| |
| |---A:
Load(hdfs://zly1.sh.intel.com:8020/user/root/split5:org.apache.pig.builtin.PigStorage)
- scope-112
|
|---C: Local Rearrange[tuple]{int}(false) - scope-92
| |
| Project[int][0] - scope-93
|
|---B: New For Each(false,false)[bag] - scope-85
| |
| Project[int][0] - scope-81
| |
| Project[int][1] - scope-83
|
|---A: New For Each(false,false,false)[bag] - scope-128
| |
| Cast[int] - scope-123
| |
| |---Project[bytearray][0] - scope-122
| |
| Cast[int] - scope-125
| |
| |---Project[bytearray][1] - scope-124
| |
| Cast[int] - scope-127
| |
| |---Project[bytearray][2] - scope-126
|
|---A:
Load(hdfs://zly1.sh.intel.com:8020/user/root/split5:org.apache.pig.builtin.PigStorage)
- scope-132--------
{code}
> Not use OperatorPlan#forceConnect in MultiQueryOptimizationSpark
> -----------------------------------------------------------------
>
> Key: PIG-4871
> URL: https://issues.apache.org/jira/browse/PIG-4871
> Project: Pig
> Issue Type: Sub-task
> Components: spark
> Reporter: liyunzhang_intel
> Assignee: liyunzhang_intel
> Fix For: spark-branch
>
> Attachments: PIG-4871.patch
>
>
> In current code base, we use OperatorPlan#forceConnect() while merge the
> physical plan of spliter and splittee in MultiQueryOptimizationSpark.
> The difference between OperatorPlan#connect and OperatorPlan#forceConnect is
> not checking whether support multiOutputs and multiInputs or not in
> forceConnect.
> {code}
> /**
> * connect from and to and ignore some judgements: like ignoring judge
> whether from operator supports multiOutputs
> * and whether to operator supports multiInputs
> *
> * @param from Operator data will flow from.
> * @param to Operator data will flow to.
> * @throws PlanException if connect from or to which is not in the plan
> */
> public void forceConnect(E from, E to) throws PlanException {
> markDirty();
> // Check that both nodes are in the plan.
> checkInPlan(from);
> checkInPlan(to);
> mFromEdges.put(from, to);
> mToEdges.put(to, from);
> }
> {code}
> Let's use an example to explain why add forceConnect before.
> {code}
> A = load './split5' AS (a0:int, a1:int, a2:int);
> B = foreach A generate a0, a1;
> C = join A by a0, B by a0;
> D = filter C by A::a1>=B::a1;
> store D into './split5.out';
> {code}
> before multiquery optimization
> {code}
> scope-37->scope-43
> scope-43
> #--------------------------------------------------
> # Spark Plan
> #--------------------------------------------------
> Spark node scope-37
> Store(hdfs://zly2.sh.intel.com:8020/tmp/temp-535495592/tmp-2029463812:org.apache.pig.impl.io.InterStorage)
> - scope-38
> |
> |---A: New For Each(false,false,false)[bag] - scope-10
> | |
> | Cast[int] - scope-2
> | |
> | |---Project[bytearray][0] - scope-1
> | |
> | Cast[int] - scope-5
> | |
> | |---Project[bytearray][1] - scope-4
> | |
> | Cast[int] - scope-8
> | |
> | |---Project[bytearray][2] - scope-7
> |
> |---A:
> Load(hdfs://zly2.sh.intel.com:8020/user/root/split5:org.apache.pig.builtin.PigStorage)
> - scope-0--------
> Spark node scope-43
> D: Store(fakefile:org.apache.pig.builtin.PigStorage) - scope-36
> |
> |---D: Filter[bag] - scope-32
> | |
> | Greater Than or Equal[boolean] - scope-35
> | |
> | |---Project[int][1] - scope-33
> | |
> | |---Project[int][4] - scope-34
> |
> |---C: New For Each(true,true)[tuple] - scope-31
> | |
> | Project[bag][1] - scope-29
> | |
> | Project[bag][2] - scope-30
> |
> |---C: Package(Packager)[tuple]{int} - scope-24
> |
> |---C: Global Rearrange[tuple] - scope-23
> |
> |---C: Local Rearrange[tuple]{int}(false) - scope-25
> | | |
> | | Project[int][0] - scope-26
> | |
> |
> |---Load(hdfs://zly2.sh.intel.com:8020/tmp/temp-535495592/tmp-2029463812:org.apache.pig.impl.io.InterStorage)
> - scope-39
> |
> |---C: Local Rearrange[tuple]{int}(false) - scope-27
> | |
> | Project[int][0] - scope-28
> |
> |---B: New For Each(false,false)[bag] - scope-20
> | |
> | Project[int][0] - scope-16
> | |
> | Project[int][1] - scope-18
> |
>
> |---Load(hdfs://zly2.sh.intel.com:8020/tmp/temp-535495592/tmp-2029463812:org.apache.pig.impl.io.InterStorage)
> - scope-41--------{code}
> after multiquery optimization
> {code}
> after multiquery optimization:
> scope-37
> #--------------------------------------------------
> # Spark Plan
> #--------------------------------------------------
> Spark node scope-37
> D: Store(fakefile:org.apache.pig.builtin.PigStorage) - scope-36
> |
> |---D: Filter[bag] - scope-32
> | |
> | Greater Than or Equal[boolean] - scope-35
> | |
> | |---Project[int][1] - scope-33
> | |
> | |---Project[int][4] - scope-34
> |
> |---C: New For Each(true,true)[tuple] - scope-31
> | |
> | Project[bag][1] - scope-29
> | |
> | Project[bag][2] - scope-30
> |
> |---C: Package(Packager)[tuple]{int} - scope-24
> |
> |---C: Global Rearrange[tuple] - scope-23
> |
> |---C: Local Rearrange[tuple]{int}(false) - scope-25
> | | |
> | | Project[int][0] - scope-26
> | |
> | |---A: New For Each(false,false,false)[bag] - scope-10
> | | |
> | | Cast[int] - scope-2
> | | |
> | | |---Project[bytearray][0] - scope-1
> | | |
> | | Cast[int] - scope-5
> | | |
> | | |---Project[bytearray][1] - scope-4
> | | |
> | | Cast[int] - scope-8
> | | |
> | | |---Project[bytearray][2] - scope-7
> | |
> | |---A:
> Load(hdfs://zly2.sh.intel.com:8020/user/root/split5:org.apache.pig.builtin.PigStorage)
> - scope-0
> |
> |---C: Local Rearrange[tuple]{int}(false) - scope-27
> | |
> | Project[int][0] - scope-28
> |
> |---B: New For Each(false,false)[bag] - scope-20
> | |
> | Project[int][0] - scope-16
> | |
> | Project[int][1] - scope-18
> |
> |---A: New For Each(false,false,false)[bag] - scope-10
> | |
> | Cast[int] - scope-2
> | |
> | |---Project[bytearray][0] - scope-1
> | |
> | Cast[int] - scope-5
> | |
> | |---Project[bytearray][1] - scope-4
> | |
> | Cast[int] - scope-8
> | |
> | |---Project[bytearray][2] - scope-7
> |
> |---A:
> Load(hdfs://zly2.sh.intel.com:8020/user/root/split5:org.apache.pig.builtin.PigStorage)
> - scope-0--------
> {code}
> We connect ForEach(scope-10) in SparkNode(scope-37) with ForEach(scope-20)
> and LocalRearrange(scope-25) in SparkNode(scope-43). The successors of
> ForEach(scope-10) are scope-20 and scope-25 after multiquery optimization.
> Here we need use OperatorPlan#forceConnect(from, to) because
> POForEach#supportsMultipleOutputs are false. *Why there is no problem in mr
> mode?* in mr, clone ForEach(scope-10) as ForEach(scope-xxx), so the size of
> successors of POForEach is always 1.
> after multiquery optimization in mr mode
> {code}
> #--------------------------------------------------
> # Map Reduce Plan
> #--------------------------------------------------
> MapReduce node scope-43
> Map Plan
> Union[tuple] - scope-44
> |
> |---C: Local Rearrange[tuple]{int}(false) - scope-25
> | | |
> | | Project[int][0] - scope-26
> | |
> | |---A: New For Each(false,false,false)[bag] - scope-53
> | | |
> | | Cast[int] - scope-48
> | | |
> | | |---Project[bytearray][0] - scope-47
> | | |
> | | Cast[int] - scope-50
> | | |
> | | |---Project[bytearray][1] - scope-49
> | | |
> | | Cast[int] - scope-52
> | | |
> | | |---Project[bytearray][2] - scope-51
> | |
> | |---A:
> Load(hdfs://zly2.sh.intel.com:8020/user/root/split5:org.apache.pig.builtin.PigStorage)
> - scope-46
> |
> |---C: Local Rearrange[tuple]{int}(false) - scope-27
> | |
> | Project[int][0] - scope-28
> |
> |---B: New For Each(false,false)[bag] - scope-20
> | |
> | Project[int][0] - scope-16
> | |
> | Project[int][1] - scope-18
> |
> |---A: New For Each(false,false,false)[bag] - scope-61
> | |
> | Cast[int] - scope-56
> | |
> | |---Project[bytearray][0] - scope-55
> | |
> | Cast[int] - scope-58
> | |
> | |---Project[bytearray][1] - scope-57
> | |
> | Cast[int] - scope-60
> | |
> | |---Project[bytearray][2] - scope-59
> |
> |---A:
> Load(hdfs://zly2.sh.intel.com:8020/user/root/split5:org.apache.pig.builtin.PigStorage)
> - scope-54--------
> Reduce Plan
> D: Store(fakefile:org.apache.pig.builtin.PigStorage) - scope-36
> |
> |---D: Filter[bag] - scope-32
> | |
> | Greater Than or Equal[boolean] - scope-35
> | |
> | |---Project[int][1] - scope-33
> | |
> | |---Project[int][4] - scope-34
> |
> |---C: Package(JoinPackager(true,true))[tuple]{int} - scope-24--------
> Global sort: false
> ----------------
> {code}
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