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stream keyBy without repartition

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Bart Wyatt

stream keyBy without repartition

(migrated from IRC)

Hello All,

My situation is this:

I have a large amount of data partitioned in kafka by "session" (natural partitioning). After I read the data, I would like to do as much as possible before incurring re-serialization or network traffic due to the size of the data. I am on 1.0.3 in the java API.

What I'd like to do is:

while maintaining the natural partitioning (so that a single thread can perform this) read data from kafka, perform a window'd fold over the incoming data keyed by a _different_ field("key") then take the product of that window'd fold and allow re-partitioning to colocate data with equivalent keys in a new partitioning scheme where they can be reduced into a final product. The hope is that the products of such a windowed fold are orders of magnitude smaller than the data that would be serialized/sent if we re-partitioned before the window'd fold.

Is there a way to .keyBy(...) such that it will act within the physical partitioning of the data and not force a re-partitioning of the data by that key?

thanks

-Bart

This e-mail may contain CONFIDENTIAL AND PROPRIETARY INFORMATION and/or PRIVILEGED AND CONFIDENTIAL COMMUNICATION intended solely for the recipient and, therefore, may not be retransmitted to any party outside of the recipient's organization without the prior written consent of the sender. If you have received this e-mail in error please notify the sender immediately by telephone or reply e-mail and destroy the original message without making a copy. Deep Silver, Inc. accepts no liability for any losses or damages resulting from infected e-mail transmissions and viruses in e-mail attachments.

Kostas Kloudas

Re: stream keyBy without repartition

Hi Bart,

From what I understand, you want to do a partial (per node) aggregation before shipping the result

for the final one at the end. In addition, the keys do not seem to change between aggregations, right?

If this is the case, this is the functionality of the Combiner in batch.

In Batch (DataSet API) this is supported, but in Streaming it is not.

If your main concern is optimizing your already up-and-running job, it would be worth sharing your code

(or an example with the same characteristics / communication patterns if the real code is not possible)

so that we can have a look and potentially find other parts of the pipeline that can be optimized.

For example, given that you are concerned with the serialization overhead, it may be worth

seeing if there are better alternatives to use.

Kostas

On May 24, 2016, at 4:22 PM, Bart Wyatt <[hidden email]> wrote:

(migrated from IRC)

Hello All,

My situation is this:
I have a large amount of data partitioned in kafka by "session" (natural partitioning). After I read the data, I would like to do as much as possible before incurring re-serialization or network traffic due to the size of the data. I am on 1.0.3 in the java API.

What I'd like to do is:

while maintaining the natural partitioning (so that a single thread can perform this) read data from kafka, perform a window'd fold over the incoming data keyed by a _different_ field("key") then take the product of that window'd fold and allow re-partitioning to colocate data with equivalent keys in a new partitioning scheme where they can be reduced into a final product. The hope is that the products of such a windowed fold are orders of magnitude smaller than the data that would be serialized/sent if we re-partitioned before the window'd fold.

Is there a way to .keyBy(...) such that it will act within the physical partitioning of the data and not force a re-partitioning of the data by that key?

thanks
-Bart

This e-mail may contain CONFIDENTIAL AND PROPRIETARY INFORMATION and/or PRIVILEGED AND CONFIDENTIAL COMMUNICATION intended solely for the recipient and, therefore, may not be retransmitted to any party outside of the recipient's organization without the prior written consent of the sender. If you have received this e-mail in error please notify the sender immediately by telephone or reply e-mail and destroy the original message without making a copy. Deep Silver, Inc. accepts no liability for any losses or damages resulting from infected e-mail transmissions and viruses in e-mail attachments.

Aljoscha Krettek

Re: stream keyBy without repartition

Hi,

what Kostas said is correct.

You can however, hack it. You would have to manually instantiate a WindowOperator and apply it on the non-keyed DataStream while still providing a key-selector (and serializer) for state. This might sound complicated but I'll try and walk you through the steps. Please let me know if anything is unclear, still.

## Creating the WindowOperator

This can be copied from WindowedStream.apply(R initialValue, FoldFunction<T, R> foldFunction, WindowFunction<R, R, K, W> function, TypeInformation<R> resultType):

DataStream<> input = ... // create stream from sources

TypeInformation<R> resultType = TypeExtractor.getFoldReturnTypes(foldFunction, input.getType(),

Utils.getCallLocationName(), true);

if (foldFunction instanceof RichFunction) {

throw new UnsupportedOperationException("FoldFunction of apply can not be a RichFunction.");

}

if (windowAssigner instanceof MergingWindowAssigner) {

throw new UnsupportedOperationException("Fold cannot be used with a merging WindowAssigner.");

}

//clean the closures

function = input.getExecutionEnvironment().clean(function);

foldFunction = input.getExecutionEnvironment().clean(foldFunction);

String callLocation = Utils.getCallLocationName();

String udfName = "WindowedStream." + callLocation;

String opName;

KeySelector<T, K> keySel = input.getKeySelector();

OneInputStreamOperator<T, R> operator;

FoldingStateDescriptor<T, R> stateDesc = new FoldingStateDescriptor<>("window-contents",

initialValue,

foldFunction,

resultType);

opName = "TriggerWindow(" + windowAssigner + ", " + stateDesc + ", " + trigger + ", " + udfName + ")";

operator = new WindowOperator<>(windowAssigner,

windowAssigner.getWindowSerializer(getExecutionEnvironment().getConfig()),

keySel,

input.getKeyType().createSerializer(getExecutionEnvironment().getConfig()),

stateDesc,

new InternalSingleValueWindowFunction<>(function),

trigger);

SingleOutputStreamOperator<> result = return input.transform(opName, resultType, operator);

## Setting the KeySelector/Serializer for the state

This can be copied from KeyedStream.transform:

OneInputTransformation<T, R> transform = (OneInputTransformation<T, R>) returnStream.getTransformation();

transform.setStateKeySelector(keySelector); // this would be your KeySelector

transform.setStateKeyType(keyType); // this would be a TypeInformation for your key type

now, "result" should be your pre-combined data that was not shuffled. On this you can key by your other type and instantiate a WindowOperator in the normal way.

Cheers,

Aljoscha

On Tue, 24 May 2016 at 17:45 Kostas Kloudas <[hidden email]> wrote:

Hi Bart,

From what I understand, you want to do a partial (per node) aggregation before shipping the result
for the final one at the end. In addition, the keys do not seem to change between aggregations, right?

If this is the case, this is the functionality of the Combiner in batch.
In Batch (DataSet API) this is supported, but in Streaming it is not.

If your main concern is optimizing your already up-and-running job, it would be worth sharing your code
(or an example with the same characteristics / communication patterns if the real code is not possible)
so that we can have a look and potentially find other parts of the pipeline that can be optimized.

For example, given that you are concerned with the serialization overhead, it may be worth
seeing if there are better alternatives to use.

Kostas

On May 24, 2016, at 4:22 PM, Bart Wyatt <[hidden email]> wrote:

(migrated from IRC)

Hello All,

My situation is this:
I have a large amount of data partitioned in kafka by "session" (natural partitioning). After I read the data, I would like to do as much as possible before incurring re-serialization or network traffic due to the size of the data. I am on 1.0.3 in the java API.

What I'd like to do is:

while maintaining the natural partitioning (so that a single thread can perform this) read data from kafka, perform a window'd fold over the incoming data keyed by a _different_ field("key") then take the product of that window'd fold and allow re-partitioning to colocate data with equivalent keys in a new partitioning scheme where they can be reduced into a final product. The hope is that the products of such a windowed fold are orders of magnitude smaller than the data that would be serialized/sent if we re-partitioned before the window'd fold.

Is there a way to .keyBy(...) such that it will act within the physical partitioning of the data and not force a re-partitioning of the data by that key?

thanks
-Bart

This e-mail may contain CONFIDENTIAL AND PROPRIETARY INFORMATION and/or PRIVILEGED AND CONFIDENTIAL COMMUNICATION intended solely for the recipient and, therefore, may not be retransmitted to any party outside of the recipient's organization without the prior written consent of the sender. If you have received this e-mail in error please notify the sender immediately by telephone or reply e-mail and destroy the original message without making a copy. Deep Silver, Inc. accepts no liability for any losses or damages resulting from infected e-mail transmissions and viruses in e-mail attachments.

Bart Wyatt

Re: stream keyBy without repartition

I will give this a shot this morning.

Considering this and the other email "Does Kafka connector leverage Kafka message keys?" which also ends up talking about hacking around KeyedStream's use of a HashPartitioner<>(...) is it worth looking in to providing a KeyedStream constructor that uses a ForwardPartitioner? This was what I was going to try this morning until you gave me a path that doesn't involve editing flink code.

-Bart

From: Aljoscha Krettek <[hidden email]>
Sent: Wednesday, May 25, 2016 4:07 AM
To: [hidden email]
Subject: Re: stream keyBy without repartition

Hi,

what Kostas said is correct.

## Creating the WindowOperator

This can be copied from WindowedStream.apply(R initialValue, FoldFunction<T, R> foldFunction, WindowFunction<R, R, K, W> function, TypeInformation<R> resultType):

DataStream<> input = ... // create stream from sources

TypeInformation<R> resultType = TypeExtractor.getFoldReturnTypes(foldFunction, input.getType(),

Utils.getCallLocationName(), true);

if (foldFunction instanceof RichFunction) {

throw new UnsupportedOperationException("FoldFunction of apply can not be a RichFunction.");

}

if (windowAssigner instanceof MergingWindowAssigner) {

throw new UnsupportedOperationException("Fold cannot be used with a merging WindowAssigner.");

}

//clean the closures

function = input.getExecutionEnvironment().clean(function);

foldFunction = input.getExecutionEnvironment().clean(foldFunction);

String callLocation = Utils.getCallLocationName();

String udfName = "WindowedStream." + callLocation;

String opName;

KeySelector<T, K> keySel = input.getKeySelector();

OneInputStreamOperator<T, R> operator;

FoldingStateDescriptor<T, R> stateDesc = new FoldingStateDescriptor<>("window-contents",

initialValue,

foldFunction,

resultType);

opName = "TriggerWindow(" + windowAssigner + ", " + stateDesc + ", " + trigger + ", " + udfName + ")";

operator = new WindowOperator<>(windowAssigner,

windowAssigner.getWindowSerializer(getExecutionEnvironment().getConfig()),

keySel,

input.getKeyType().createSerializer(getExecutionEnvironment().getConfig()),

stateDesc,

new InternalSingleValueWindowFunction<>(function),

trigger);

SingleOutputStreamOperator<> result = return input.transform(opName, resultType, operator);

## Setting the KeySelector/Serializer for the state

This can be copied from KeyedStream.transform:

OneInputTransformation<T, R> transform = (OneInputTransformation<T, R>) returnStream.getTransformation();

transform.setStateKeySelector(keySelector); // this would be your KeySelector

transform.setStateKeyType(keyType); // this would be a TypeInformation for your key type

now, "result" should be your pre-combined data that was not shuffled. On this you can key by your other type and instantiate a WindowOperator in the normal way.

Cheers,

Aljoscha

On Tue, 24 May 2016 at 17:45 Kostas Kloudas <[hidden email]> wrote:

Hi Bart,

From what I understand, you want to do a partial (per node) aggregation before shipping the result

for the final one at the end. In addition, the keys do not seem to change between aggregations, right?

If this is the case, this is the functionality of the Combiner in batch.

In Batch (DataSet API) this is supported, but in Streaming it is not.

If your main concern is optimizing your already up-and-running job, it would be worth sharing your code

(or an example with the same characteristics / communication patterns if the real code is not possible)

so that we can have a look and potentially find other parts of the pipeline that can be optimized.

For example, given that you are concerned with the serialization overhead, it may be worth

seeing if there are better alternatives to use.

Kostas

On May 24, 2016, at 4:22 PM, Bart Wyatt <[hidden email]> wrote:

(migrated from IRC)

Hello All,

My situation is this:

I have a large amount of data partitioned in kafka by "session" (natural partitioning). After I read the data, I would like to do as much as possible before incurring re-serialization or network traffic due to the size of the data. I am on 1.0.3 in the java API.

What I'd like to do is:

while maintaining the natural partitioning (so that a single thread can perform this) read data from kafka, perform a window'd fold over the incoming data keyed by a _different_ field("key") then take the product of that window'd fold and allow re-partitioning to colocate data with equivalent keys in a new partitioning scheme where they can be reduced into a final product. The hope is that the products of such a windowed fold are orders of magnitude smaller than the data that would be serialized/sent if we re-partitioned before the window'd fold.

Is there a way to .keyBy(...) such that it will act within the physical partitioning of the data and not force a re-partitioning of the data by that key?

thanks

-Bart

This e-mail may contain CONFIDENTIAL AND PROPRIETARY INFORMATION and/or PRIVILEGED AND CONFIDENTIAL COMMUNICATION intended solely for the recipient and, therefore, may not be retransmitted to any party outside of the recipient's organization without the prior written consent of the sender. If you have received this e-mail in error please notify the sender immediately by telephone or reply e-mail and destroy the original message without making a copy. Deep Silver, Inc. accepts no liability for any losses or damages resulting from infected e-mail transmissions and viruses in e-mail attachments.

Aljoscha Krettek

Re: stream keyBy without repartition

In the long run we probably have to provide a hook in the API for this, yes.

On Wed, 25 May 2016 at 15:54 Bart Wyatt <[hidden email]> wrote:

I will give this a shot this morning.

Considering this and the other email "Does Kafka connector leverage Kafka message keys?" which also ends up talking about hacking around KeyedStream's use of a HashPartitioner<>(...) is it worth looking in to providing a KeyedStream constructor that uses a ForwardPartitioner? This was what I was going to try this morning until you gave me a path that doesn't involve editing flink code.

-Bart

From: Aljoscha Krettek <[hidden email]>
Sent: Wednesday, May 25, 2016 4:07 AM
To: [hidden email]
Subject: Re: stream keyBy without repartition

Hi,
what Kostas said is correct.

You can however, hack it. You would have to manually instantiate a WindowOperator and apply it on the non-keyed DataStream while still providing a key-selector (and serializer) for state. This might sound complicated but I'll try and walk you through the steps. Please let me know if anything is unclear, still.

## Creating the WindowOperator

This can be copied from WindowedStream.apply(R initialValue, FoldFunction<T, R> foldFunction, WindowFunction<R, R, K, W> function, TypeInformation<R> resultType):

DataStream<> input = ... // create stream from sources

TypeInformation<R> resultType = TypeExtractor.getFoldReturnTypes(foldFunction, input.getType(),

Utils.getCallLocationName(), true);

if (foldFunction instanceof RichFunction) {

throw new UnsupportedOperationException("FoldFunction of apply can not be a RichFunction.");

}

if (windowAssigner instanceof MergingWindowAssigner) {

throw new UnsupportedOperationException("Fold cannot be used with a merging WindowAssigner.");

}

//clean the closures

function = input.getExecutionEnvironment().clean(function);

foldFunction = input.getExecutionEnvironment().clean(foldFunction);

String callLocation = Utils.getCallLocationName();

String udfName = "WindowedStream." + callLocation;

String opName;

KeySelector<T, K> keySel = input.getKeySelector();

OneInputStreamOperator<T, R> operator;

FoldingStateDescriptor<T, R> stateDesc = new FoldingStateDescriptor<>("window-contents",

initialValue,

foldFunction,

resultType);

opName = "TriggerWindow(" + windowAssigner + ", " + stateDesc + ", " + trigger + ", " + udfName + ")";

operator = new WindowOperator<>(windowAssigner,

windowAssigner.getWindowSerializer(getExecutionEnvironment().getConfig()),

keySel,

input.getKeyType().createSerializer(getExecutionEnvironment().getConfig()),

stateDesc,

new InternalSingleValueWindowFunction<>(function),

trigger);

SingleOutputStreamOperator<> result = return input.transform(opName, resultType, operator);

## Setting the KeySelector/Serializer for the state

This can be copied from KeyedStream.transform:

OneInputTransformation<T, R> transform = (OneInputTransformation<T, R>) returnStream.getTransformation();

transform.setStateKeySelector(keySelector); // this would be your KeySelector

transform.setStateKeyType(keyType); // this would be a TypeInformation for your key type

now, "result" should be your pre-combined data that was not shuffled. On this you can key by your other type and instantiate a WindowOperator in the normal way.

Cheers,

Aljoscha

On Tue, 24 May 2016 at 17:45 Kostas Kloudas <[hidden email]> wrote:

Hi Bart,

From what I understand, you want to do a partial (per node) aggregation before shipping the result

for the final one at the end. In addition, the keys do not seem to change between aggregations, right?

If this is the case, this is the functionality of the Combiner in batch.

In Batch (DataSet API) this is supported, but in Streaming it is not.

If your main concern is optimizing your already up-and-running job, it would be worth sharing your code

(or an example with the same characteristics / communication patterns if the real code is not possible)

so that we can have a look and potentially find other parts of the pipeline that can be optimized.

For example, given that you are concerned with the serialization overhead, it may be worth

seeing if there are better alternatives to use.

Kostas

On May 24, 2016, at 4:22 PM, Bart Wyatt <[hidden email]> wrote:

(migrated from IRC)

Hello All,

My situation is this:

I have a large amount of data partitioned in kafka by "session" (natural partitioning). After I read the data, I would like to do as much as possible before incurring re-serialization or network traffic due to the size of the data. I am on 1.0.3 in the java API.

What I'd like to do is:

while maintaining the natural partitioning (so that a single thread can perform this) read data from kafka, perform a window'd fold over the incoming data keyed by a _different_ field("key") then take the product of that window'd fold and allow re-partitioning to colocate data with equivalent keys in a new partitioning scheme where they can be reduced into a final product. The hope is that the products of such a windowed fold are orders of magnitude smaller than the data that would be serialized/sent if we re-partitioned before the window'd fold.

Is there a way to .keyBy(...) such that it will act within the physical partitioning of the data and not force a re-partitioning of the data by that key?

thanks

-Bart

This e-mail may contain CONFIDENTIAL AND PROPRIETARY INFORMATION and/or PRIVILEGED AND CONFIDENTIAL COMMUNICATION intended solely for the recipient and, therefore, may not be retransmitted to any party outside of the recipient's organization without the prior written consent of the sender. If you have received this e-mail in error please notify the sender immediately by telephone or reply e-mail and destroy the original message without making a copy. Deep Silver, Inc. accepts no liability for any losses or damages resulting from infected e-mail transmissions and viruses in e-mail attachments.

This e-mail may contain CONFIDENTIAL AND PROPRIETARY INFORMATION and/or PRIVILEGED AND CONFIDENTIAL COMMUNICATION intended solely for the recipient and, therefore, may not be retransmitted to any party outside of the recipient's organization without the prior written consent of the sender. If you have received this e-mail in error please notify the sender immediately by telephone or reply e-mail and destroy the original message without making a copy. Deep Silver, Inc. accepts no liability for any losses or damages resulting from infected e-mail transmissions and viruses in e-mail attachments.

Bart Wyatt

Re: stream keyBy without repartition

Aljoscha,

Thanks for the pointers. I was able to get a pretty simple utility class up and running that gives me basic keyed fold/reduce/windowedFold/windowedReduce operations that don't change the partitioning.

This will be invaluable until an official feature is supported

Cheers,

-Bart

From: Aljoscha Krettek <[hidden email]>
Sent: Wednesday, May 25, 2016 9:14 AM
To: [hidden email]
Subject: Re: stream keyBy without repartition

In the long run we probably have to provide a hook in the API for this, yes.

On Wed, 25 May 2016 at 15:54 Bart Wyatt <[hidden email]> wrote:

I will give this a shot this morning.

Considering this and the other email "Does Kafka connector leverage Kafka message keys?" which also ends up talking about hacking around KeyedStream's use of a HashPartitioner<>(...) is it worth looking in to providing a KeyedStream constructor that uses a ForwardPartitioner? This was what I was going to try this morning until you gave me a path that doesn't involve editing flink code.

-Bart

From: Aljoscha Krettek <[hidden email]>
Sent: Wednesday, May 25, 2016 4:07 AM
To: [hidden email]
Subject: Re: stream keyBy without repartition

Hi,
what Kostas said is correct.

You can however, hack it. You would have to manually instantiate a WindowOperator and apply it on the non-keyed DataStream while still providing a key-selector (and serializer) for state. This might sound complicated but I'll try and walk you through the steps. Please let me know if anything is unclear, still.

## Creating the WindowOperator

This can be copied from WindowedStream.apply(R initialValue, FoldFunction<T, R> foldFunction, WindowFunction<R, R, K, W> function, TypeInformation<R> resultType):

DataStream<> input = ... // create stream from sources

TypeInformation<R> resultType = TypeExtractor.getFoldReturnTypes(foldFunction, input.getType(),

Utils.getCallLocationName(), true);

if (foldFunction instanceof RichFunction) {

throw new UnsupportedOperationException("FoldFunction of apply can not be a RichFunction.");

}

if (windowAssigner instanceof MergingWindowAssigner) {

throw new UnsupportedOperationException("Fold cannot be used with a merging WindowAssigner.");

}

//clean the closures

function = input.getExecutionEnvironment().clean(function);

foldFunction = input.getExecutionEnvironment().clean(foldFunction);

String callLocation = Utils.getCallLocationName();

String udfName = "WindowedStream." + callLocation;

String opName;

KeySelector<T, K> keySel = input.getKeySelector();

OneInputStreamOperator<T, R> operator;

FoldingStateDescriptor<T, R> stateDesc = new FoldingStateDescriptor<>("window-contents",

initialValue,

foldFunction,

resultType);

opName = "TriggerWindow(" + windowAssigner + ", " + stateDesc + ", " + trigger + ", " + udfName + ")";

operator = new WindowOperator<>(windowAssigner,

windowAssigner.getWindowSerializer(getExecutionEnvironment().getConfig()),

keySel,

input.getKeyType().createSerializer(getExecutionEnvironment().getConfig()),

stateDesc,

new InternalSingleValueWindowFunction<>(function),

trigger);

SingleOutputStreamOperator<> result = return input.transform(opName, resultType, operator);

## Setting the KeySelector/Serializer for the state

This can be copied from KeyedStream.transform:

OneInputTransformation<T, R> transform = (OneInputTransformation<T, R>) returnStream.getTransformation();

transform.setStateKeySelector(keySelector); // this would be your KeySelector

transform.setStateKeyType(keyType); // this would be a TypeInformation for your key type

now, "result" should be your pre-combined data that was not shuffled. On this you can key by your other type and instantiate a WindowOperator in the normal way.

Cheers,

Aljoscha

On Tue, 24 May 2016 at 17:45 Kostas Kloudas <[hidden email]> wrote:

Hi Bart,

From what I understand, you want to do a partial (per node) aggregation before shipping the result

for the final one at the end. In addition, the keys do not seem to change between aggregations, right?

If this is the case, this is the functionality of the Combiner in batch.

In Batch (DataSet API) this is supported, but in Streaming it is not.

If your main concern is optimizing your already up-and-running job, it would be worth sharing your code

(or an example with the same characteristics / communication patterns if the real code is not possible)

so that we can have a look and potentially find other parts of the pipeline that can be optimized.

For example, given that you are concerned with the serialization overhead, it may be worth

seeing if there are better alternatives to use.

Kostas

On May 24, 2016, at 4:22 PM, Bart Wyatt <[hidden email]> wrote:

(migrated from IRC)

Hello All,

My situation is this:

I have a large amount of data partitioned in kafka by "session" (natural partitioning). After I read the data, I would like to do as much as possible before incurring re-serialization or network traffic due to the size of the data. I am on 1.0.3 in the java API.

What I'd like to do is:

while maintaining the natural partitioning (so that a single thread can perform this) read data from kafka, perform a window'd fold over the incoming data keyed by a _different_ field("key") then take the product of that window'd fold and allow re-partitioning to colocate data with equivalent keys in a new partitioning scheme where they can be reduced into a final product. The hope is that the products of such a windowed fold are orders of magnitude smaller than the data that would be serialized/sent if we re-partitioned before the window'd fold.

Is there a way to .keyBy(...) such that it will act within the physical partitioning of the data and not force a re-partitioning of the data by that key?

thanks

-Bart

This e-mail may contain CONFIDENTIAL AND PROPRIETARY INFORMATION and/or PRIVILEGED AND CONFIDENTIAL COMMUNICATION intended solely for the recipient and, therefore, may not be retransmitted to any party outside of the recipient's organization without the prior written consent of the sender. If you have received this e-mail in error please notify the sender immediately by telephone or reply e-mail and destroy the original message without making a copy. Deep Silver, Inc. accepts no liability for any losses or damages resulting from infected e-mail transmissions and viruses in e-mail attachments.

This e-mail may contain CONFIDENTIAL AND PROPRIETARY INFORMATION and/or PRIVILEGED AND CONFIDENTIAL COMMUNICATION intended solely for the recipient and, therefore, may not be retransmitted to any party outside of the recipient's organization without the prior written consent of the sender. If you have received this e-mail in error please notify the sender immediately by telephone or reply e-mail and destroy the original message without making a copy. Deep Silver, Inc. accepts no liability for any losses or damages resulting from infected e-mail transmissions and viruses in e-mail attachments.