When is Ant and Dec's Saturday Night Takeaway coming back to TV?
Ant and Dec are returning to our screens tonight as the 2019 series of I’m A Celebrity kicks off.
The pair are reunited on the show this year after Ant took a year off in 2018 to focus on his health following his drink driving conviction and prescription drugs battle – leaving Holly Willoughby to step in and co-host alongside Dec.
They’ve already been back together fronting Britain’s Got Talent back in the spring, and it’s not too long before they’ll also be bringing Saturday Night Takeaway back to the screen after a two-year break.
But just when will that show be returning, following its year off?
Here’s what you need to know…
When is Ant and Dec’s Saturday Night Takeaway coming back to TV?
It seems like ages since we last saw Saturday Night Takeaway on our tellies, but the good news is we don’t have to wait too much longer for its return.
That’s because it’s been confirmed that the show will be back in 2020.
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Although no start date has yet been announced for the next series, previous seasons have aired around February/March time.
So we’re keeping everything crossed that the new series will follow the same pattern – meaning it’s only a matter of months before it comes back.
Ant and Dec recently confirmed work had begun on the new series of the show, sharing a snap from the first production meeting as they said: ‘First meeting for Saturday Night Takeaway 2020…exciting times!’ along with details of how viewers can get involved.
‘There’s a real buzz around the show,’ a source teased about the show’s return back in August. ‘It promises to be bigger and better than ever.’
What time is I’m A Celebrity on ITV tonight?
The new series kicks off on ITV on Sunday night at 9pm.
Ant has recently opened up about how excited he is to be back on the show, telling The Sun: ‘It’s a brilliant place to work and a brilliant part of the world to be in.
‘Plus, I’m back with my best friend. I can’t wait.’
Got a showbiz story?
If you've got a story, video or pictures get in touch with the Metro.co.uk Entertainment team by emailing us<EMAIL_ADDRESS>calling 020 3615 2145 or by visiting our Submit Stuff page - we'd love to hear from you.


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public class PairRDDFunctions<K,V>
extends Object
implements org.apache.spark.internal.Logging, scala.Serializable
| Constructor and Description |
|---|
PairRDDFunctions(RDD<scala.Tuple2<K,V>> self,
scala.reflect.ClassTag<K> kt,
scala.reflect.ClassTag<V> vt,
scala.math.Ordering<K> ord) |
| Modifier and Type | Method and Description |
|---|---|
<U> RDD<scala.Tuple2<K,U>> |
aggregateByKey(U zeroValue,
scala.Function2<U,V,U> seqOp,
scala.Function2<U,U,U> combOp,
scala.reflect.ClassTag<U> evidence$3)
Aggregate the values of each key, using given combine functions and a neutral "zero value".
|
<U> RDD<scala.Tuple2<K,U>> |
aggregateByKey(U zeroValue,
int numPartitions,
scala.Function2<U,V,U> seqOp,
scala.Function2<U,U,U> combOp,
scala.reflect.ClassTag<U> evidence$2)
Aggregate the values of each key, using given combine functions and a neutral "zero value".
|
<U> RDD<scala.Tuple2<K,U>> |
aggregateByKey(U zeroValue,
Partitioner partitioner,
scala.Function2<U,V,U> seqOp,
scala.Function2<U,U,U> combOp,
scala.reflect.ClassTag<U> evidence$1)
Aggregate the values of each key, using given combine functions and a neutral "zero value".
|
<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>> |
cogroup(RDD<scala.Tuple2<K,W>> other)
For each key k in
this or other , return a resulting RDD that contains a tuple with the
list of values for that key in this as well as other . |
<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>> |
cogroup(RDD<scala.Tuple2<K,W>> other,
int numPartitions)
For each key k in
this or other , return a resulting RDD that contains a tuple with the
list of values for that key in this as well as other . |
<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>> |
cogroup(RDD<scala.Tuple2<K,W>> other,
Partitioner partitioner)
For each key k in
this or other , return a resulting RDD that contains a tuple with the
list of values for that key in this as well as other . |
<W1,W2> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>> |
cogroup(RDD<scala.Tuple2<K,W1>> other1,
RDD<scala.Tuple2<K,W2>> other2)
For each key k in
this or other1 or other2 , return a resulting RDD that contains a
tuple with the list of values for that key in this , other1 and other2 . |
<W1,W2> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>> |
cogroup(RDD<scala.Tuple2<K,W1>> other1,
RDD<scala.Tuple2<K,W2>> other2,
int numPartitions)
For each key k in
this or other1 or other2 , return a resulting RDD that contains a
tuple with the list of values for that key in this , other1 and other2 . |
<W1,W2> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>> |
cogroup(RDD<scala.Tuple2<K,W1>> other1,
RDD<scala.Tuple2<K,W2>> other2,
Partitioner partitioner)
For each key k in
this or other1 or other2 , return a resulting RDD that contains a
tuple with the list of values for that key in this , other1 and other2 . |
<W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>> |
cogroup(RDD<scala.Tuple2<K,W1>> other1,
RDD<scala.Tuple2<K,W2>> other2,
RDD<scala.Tuple2<K,W3>> other3)
For each key k in
this or other1 or other2 or other3 ,
return a resulting RDD that contains a tuple with the list of values
for that key in this , other1 , other2 and other3 . |
<W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>> |
cogroup(RDD<scala.Tuple2<K,W1>> other1,
RDD<scala.Tuple2<K,W2>> other2,
RDD<scala.Tuple2<K,W3>> other3,
int numPartitions)
For each key k in
this or other1 or other2 or other3 ,
return a resulting RDD that contains a tuple with the list of values
for that key in this , other1 , other2 and other3 . |
<W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>> |
cogroup(RDD<scala.Tuple2<K,W1>> other1,
RDD<scala.Tuple2<K,W2>> other2,
RDD<scala.Tuple2<K,W3>> other3,
Partitioner partitioner)
For each key k in
this or other1 or other2 or other3 ,
return a resulting RDD that contains a tuple with the list of values
for that key in this , other1 , other2 and other3 . |
scala.collection.Map<K,V> |
collectAsMap()
Return the key-value pairs in this RDD to the master as a Map.
|
<C> RDD<scala.Tuple2<K,C>> |
combineByKey(scala.Function1<V,C> createCombiner,
scala.Function2<C,V,C> mergeValue,
scala.Function2<C,C,C> mergeCombiners)
Simplified version of combineByKeyWithClassTag that hash-partitions the resulting RDD using the
existing partitioner/parallelism level.
|
<C> RDD<scala.Tuple2<K,C>> |
combineByKey(scala.Function1<V,C> createCombiner,
scala.Function2<C,V,C> mergeValue,
scala.Function2<C,C,C> mergeCombiners,
int numPartitions)
Simplified version of combineByKeyWithClassTag that hash-partitions the output RDD.
|
<C> RDD<scala.Tuple2<K,C>> |
combineByKey(scala.Function1<V,C> createCombiner,
scala.Function2<C,V,C> mergeValue,
scala.Function2<C,C,C> mergeCombiners,
Partitioner partitioner,
boolean mapSideCombine,
Serializer serializer)
Generic function to combine the elements for each key using a custom set of aggregation
functions.
|
<C> RDD<scala.Tuple2<K,C>> |
combineByKeyWithClassTag(scala.Function1<V,C> createCombiner,
scala.Function2<C,V,C> mergeValue,
scala.Function2<C,C,C> mergeCombiners,
scala.reflect.ClassTag<C> ct)
Simplified version of combineByKeyWithClassTag that hash-partitions the resulting RDD using the
existing partitioner/parallelism level.
|
<C> RDD<scala.Tuple2<K,C>> |
combineByKeyWithClassTag(scala.Function1<V,C> createCombiner,
scala.Function2<C,V,C> mergeValue,
scala.Function2<C,C,C> mergeCombiners,
int numPartitions,
scala.reflect.ClassTag<C> ct)
Simplified version of combineByKeyWithClassTag that hash-partitions the output RDD.
|
<C> RDD<scala.Tuple2<K,C>> |
combineByKeyWithClassTag(scala.Function1<V,C> createCombiner,
scala.Function2<C,V,C> mergeValue,
scala.Function2<C,C,C> mergeCombiners,
Partitioner partitioner,
boolean mapSideCombine,
Serializer serializer,
scala.reflect.ClassTag<C> ct)
Generic function to combine the elements for each key using a custom set of aggregation
functions.
|
RDD<scala.Tuple2<K,Object>> |
countApproxDistinctByKey(double relativeSD)
Return approximate number of distinct values for each key in this RDD.
|
RDD<scala.Tuple2<K,Object>> |
countApproxDistinctByKey(double relativeSD,
int numPartitions)
Return approximate number of distinct values for each key in this RDD.
|
RDD<scala.Tuple2<K,Object>> |
countApproxDistinctByKey(double relativeSD,
Partitioner partitioner)
Return approximate number of distinct values for each key in this RDD.
|
RDD<scala.Tuple2<K,Object>> |
countApproxDistinctByKey(int p,
int sp,
Partitioner partitioner)
Return approximate number of distinct values for each key in this RDD.
|
scala.collection.Map<K,Object> |
countByKey()
Count the number of elements for each key, collecting the results to a local Map.
|
PartialResult<scala.collection.Map<K,BoundedDouble>> |
countByKeyApprox(long timeout,
double confidence)
Approximate version of countByKey that can return a partial result if it does
not finish within a timeout.
|
<U> RDD<scala.Tuple2<K,U>> |
flatMapValues(scala.Function1<V,scala.collection.TraversableOnce<U>> f)
Pass each value in the key-value pair RDD through a flatMap function without changing the
keys; this also retains the original RDD's partitioning.
|
RDD<scala.Tuple2<K,V>> |
foldByKey(V zeroValue,
scala.Function2<V,V,V> func)
Merge the values for each key using an associative function and a neutral "zero value" which
may be added to the result an arbitrary number of times, and must not change the result
(e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
|
RDD<scala.Tuple2<K,V>> |
foldByKey(V zeroValue,
int numPartitions,
scala.Function2<V,V,V> func)
Merge the values for each key using an associative function and a neutral "zero value" which
may be added to the result an arbitrary number of times, and must not change the result
(e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
|
RDD<scala.Tuple2<K,V>> |
foldByKey(V zeroValue,
Partitioner partitioner,
scala.Function2<V,V,V> func)
Merge the values for each key using an associative function and a neutral "zero value" which
may be added to the result an arbitrary number of times, and must not change the result
(e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
|
<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,scala.Option<W>>>> |
fullOuterJoin(RDD<scala.Tuple2<K,W>> other)
Perform a full outer join of
this and other . |
<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,scala.Option<W>>>> |
fullOuterJoin(RDD<scala.Tuple2<K,W>> other,
int numPartitions)
Perform a full outer join of
this and other . |
<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,scala.Option<W>>>> |
fullOuterJoin(RDD<scala.Tuple2<K,W>> other,
Partitioner partitioner)
Perform a full outer join of
this and other . |
RDD<scala.Tuple2<K,scala.collection.Iterable<V>>> |
groupByKey()
Group the values for each key in the RDD into a single sequence.
|
RDD<scala.Tuple2<K,scala.collection.Iterable<V>>> |
groupByKey(int numPartitions)
Group the values for each key in the RDD into a single sequence.
|
RDD<scala.Tuple2<K,scala.collection.Iterable<V>>> |
groupByKey(Partitioner partitioner)
Group the values for each key in the RDD into a single sequence.
|
<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>> |
groupWith(RDD<scala.Tuple2<K,W>> other)
Alias for cogroup.
|
<W1,W2> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>> |
groupWith(RDD<scala.Tuple2<K,W1>> other1,
RDD<scala.Tuple2<K,W2>> other2)
Alias for cogroup.
|
<W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>> |
groupWith(RDD<scala.Tuple2<K,W1>> other1,
RDD<scala.Tuple2<K,W2>> other2,
RDD<scala.Tuple2<K,W3>> other3)
Alias for cogroup.
|
<W> RDD<scala.Tuple2<K,scala.Tuple2<V,W>>> |
join(RDD<scala.Tuple2<K,W>> other)
Return an RDD containing all pairs of elements with matching keys in
this and other . |
<W> RDD<scala.Tuple2<K,scala.Tuple2<V,W>>> |
join(RDD<scala.Tuple2<K,W>> other,
int numPartitions)
Return an RDD containing all pairs of elements with matching keys in
this and other . |
<W> RDD<scala.Tuple2<K,scala.Tuple2<V,W>>> |
join(RDD<scala.Tuple2<K,W>> other,
Partitioner partitioner)
Return an RDD containing all pairs of elements with matching keys in
this and other . |
RDD<K> |
keys()
Return an RDD with the keys of each tuple.
|
<W> RDD<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>> |
leftOuterJoin(RDD<scala.Tuple2<K,W>> other)
Perform a left outer join of
this and other . |
<W> RDD<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>> |
leftOuterJoin(RDD<scala.Tuple2<K,W>> other,
int numPartitions)
Perform a left outer join of
this and other . |
<W> RDD<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>> |
leftOuterJoin(RDD<scala.Tuple2<K,W>> other,
Partitioner partitioner)
Perform a left outer join of
this and other . |
scala.collection.Seq<V> |
lookup(K key)
Return the list of values in the RDD for key
key . |
<U> RDD<scala.Tuple2<K,U>> |
mapValues(scala.Function1<V,U> f)
Pass each value in the key-value pair RDD through a map function without changing the keys;
this also retains the original RDD's partitioning.
|
RDD<scala.Tuple2<K,V>> |
partitionBy(Partitioner partitioner)
Return a copy of the RDD partitioned using the specified partitioner.
|
RDD<scala.Tuple2<K,V>> |
reduceByKey(scala.Function2<V,V,V> func)
Merge the values for each key using an associative and commutative reduce function.
|
RDD<scala.Tuple2<K,V>> |
reduceByKey(scala.Function2<V,V,V> func,
int numPartitions)
Merge the values for each key using an associative and commutative reduce function.
|
RDD<scala.Tuple2<K,V>> |
reduceByKey(Partitioner partitioner,
scala.Function2<V,V,V> func)
Merge the values for each key using an associative and commutative reduce function.
|
scala.collection.Map<K,V> |
reduceByKeyLocally(scala.Function2<V,V,V> func)
Merge the values for each key using an associative and commutative reduce function, but return
the results immediately to the master as a Map.
|
<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>> |
rightOuterJoin(RDD<scala.Tuple2<K,W>> other)
Perform a right outer join of
this and other . |
<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>> |
rightOuterJoin(RDD<scala.Tuple2<K,W>> other,
int numPartitions)
Perform a right outer join of
this and other . |
<W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>> |
rightOuterJoin(RDD<scala.Tuple2<K,W>> other,
Partitioner partitioner)
Perform a right outer join of
this and other . |
RDD<scala.Tuple2<K,V>> |
sampleByKey(boolean withReplacement,
scala.collection.Map<K,Object> fractions,
long seed)
Return a subset of this RDD sampled by key (via stratified sampling).
|
RDD<scala.Tuple2<K,V>> |
sampleByKeyExact(boolean withReplacement,
scala.collection.Map<K,Object> fractions,
long seed)
Return a subset of this RDD sampled by key (via stratified sampling) containing exactly
math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key).
|
void |
saveAsHadoopDataset(org.apache.hadoop.mapred.JobConf conf)
Output the RDD to any Hadoop-supported storage system, using a Hadoop JobConf object for
that storage system.
|
void |
saveAsHadoopFile(String path,
Class<?> keyClass,
Class<?> valueClass,
Class<? extends org.apache.hadoop.mapred.OutputFormat<?,?>> outputFormatClass,
Class<? extends org.apache.hadoop.io.compress.CompressionCodec> codec)
Output the RDD to any Hadoop-supported file system, using a Hadoop
OutputFormat class
supporting the key and value types K and V in this RDD. |
void |
saveAsHadoopFile(String path,
Class<?> keyClass,
Class<?> valueClass,
Class<? extends org.apache.hadoop.mapred.OutputFormat<?,?>> outputFormatClass,
org.apache.hadoop.mapred.JobConf conf,
scala.Option<Class<? extends org.apache.hadoop.io.compress.CompressionCodec>> codec)
Output the RDD to any Hadoop-supported file system, using a Hadoop
OutputFormat class
supporting the key and value types K and V in this RDD. |
<F extends org.apache.hadoop.mapred.OutputFormat<K,V>> |
saveAsHadoopFile(String path,
Class<? extends org.apache.hadoop.io.compress.CompressionCodec> codec,
scala.reflect.ClassTag<F> fm)
Output the RDD to any Hadoop-supported file system, using a Hadoop
OutputFormat class
supporting the key and value types K and V in this RDD. |
<F extends org.apache.hadoop.mapred.OutputFormat<K,V>> |
saveAsHadoopFile(String path,
scala.reflect.ClassTag<F> fm)
Output the RDD to any Hadoop-supported file system, using a Hadoop
OutputFormat class
supporting the key and value types K and V in this RDD. |
void |
saveAsNewAPIHadoopDataset(org.apache.hadoop.conf.Configuration conf)
Output the RDD to any Hadoop-supported storage system with new Hadoop API, using a Hadoop
Configuration object for that storage system.
|
void |
saveAsNewAPIHadoopFile(String path,
Class<?> keyClass,
Class<?> valueClass,
Class<? extends org.apache.hadoop.mapreduce.OutputFormat<?,?>> outputFormatClass,
org.apache.hadoop.conf.Configuration conf)
Output the RDD to any Hadoop-supported file system, using a new Hadoop API
OutputFormat
(mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD. |
<F extends org.apache.hadoop.mapreduce.OutputFormat<K,V>> |
saveAsNewAPIHadoopFile(String path,
scala.reflect.ClassTag<F> fm)
Output the RDD to any Hadoop-supported file system, using a new Hadoop API
OutputFormat
(mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD. |
<W> RDD<scala.Tuple2<K,V>> |
subtractByKey(RDD<scala.Tuple2<K,W>> other,
scala.reflect.ClassTag<W> evidence$4)
Return an RDD with the pairs from
this whose keys are not in other . |
<W> RDD<scala.Tuple2<K,V>> |
subtractByKey(RDD<scala.Tuple2<K,W>> other,
int numPartitions,
scala.reflect.ClassTag<W> evidence$5)
Return an RDD with the pairs from
this whose keys are not in other . |
<W> RDD<scala.Tuple2<K,V>> |
subtractByKey(RDD<scala.Tuple2<K,W>> other,
Partitioner p,
scala.reflect.ClassTag<W> evidence$6)
Return an RDD with the pairs from
this whose keys are not in other . |
RDD<V> |
values()
Return an RDD with the values of each tuple.
|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize
public <C> RDD<scala.Tuple2<K,C>> combineByKeyWithClassTag(scala.Function1<V,C> createCombiner, scala.Function2<C,V,C> mergeValue, scala.Function2<C,C,C> mergeCombiners, Partitioner partitioner, boolean mapSideCombine, Serializer serializer, scala.reflect.ClassTag<C> ct)
Users provide three functions:
- createCombiner
, which turns a V into a C (e.g., creates a one-element list)
- mergeValue
, to merge a V into a C (e.g., adds it to the end of a list)
- mergeCombiners
, to combine two C's into a single one.
In addition, users can control the partitioning of the output RDD, and whether to perform map-side aggregation (if a mapper can produce multiple items with the same key).
createCombiner
- (undocumented)mergeValue
- (undocumented)mergeCombiners
- (undocumented)partitioner
- (undocumented)mapSideCombine
- (undocumented)serializer
- (undocumented)ct
- (undocumented)public <C> RDD<scala.Tuple2<K,C>> combineByKey(scala.Function1<V,C> createCombiner, scala.Function2<C,V,C> mergeValue, scala.Function2<C,C,C> mergeCombiners, Partitioner partitioner, boolean mapSideCombine, Serializer serializer)
createCombiner
- (undocumented)mergeValue
- (undocumented)mergeCombiners
- (undocumented)partitioner
- (undocumented)mapSideCombine
- (undocumented)serializer
- (undocumented)combineByKeyWithClassTag
public <C> RDD<scala.Tuple2<K,C>> combineByKey(scala.Function1<V,C> createCombiner, scala.Function2<C,V,C> mergeValue, scala.Function2<C,C,C> mergeCombiners, int numPartitions)
createCombiner
- (undocumented)mergeValue
- (undocumented)mergeCombiners
- (undocumented)numPartitions
- (undocumented)combineByKeyWithClassTag
public <C> RDD<scala.Tuple2<K,C>> combineByKeyWithClassTag(scala.Function1<V,C> createCombiner, scala.Function2<C,V,C> mergeValue, scala.Function2<C,C,C> mergeCombiners, int numPartitions, scala.reflect.ClassTag<C> ct)
createCombiner
- (undocumented)mergeValue
- (undocumented)mergeCombiners
- (undocumented)numPartitions
- (undocumented)ct
- (undocumented)public <U> RDD<scala.Tuple2<K,U>> aggregateByKey(U zeroValue, Partitioner partitioner, scala.Function2<U,V,U> seqOp, scala.Function2<U,U,U> combOp, scala.reflect.ClassTag<U> evidence$1)
zeroValue
- (undocumented)partitioner
- (undocumented)seqOp
- (undocumented)combOp
- (undocumented)evidence$1
- (undocumented)public <U> RDD<scala.Tuple2<K,U>> aggregateByKey(U zeroValue, int numPartitions, scala.Function2<U,V,U> seqOp, scala.Function2<U,U,U> combOp, scala.reflect.ClassTag<U> evidence$2)
zeroValue
- (undocumented)numPartitions
- (undocumented)seqOp
- (undocumented)combOp
- (undocumented)evidence$2
- (undocumented)public <U> RDD<scala.Tuple2<K,U>> aggregateByKey(U zeroValue, scala.Function2<U,V,U> seqOp, scala.Function2<U,U,U> combOp, scala.reflect.ClassTag<U> evidence$3)
zeroValue
- (undocumented)seqOp
- (undocumented)combOp
- (undocumented)evidence$3
- (undocumented)public RDD<scala.Tuple2<K,V>> foldByKey(V zeroValue, Partitioner partitioner, scala.Function2<V,V,V> func)
zeroValue
- (undocumented)partitioner
- (undocumented)func
- (undocumented)public RDD<scala.Tuple2<K,V>> foldByKey(V zeroValue, int numPartitions, scala.Function2<V,V,V> func)
zeroValue
- (undocumented)numPartitions
- (undocumented)func
- (undocumented)public RDD<scala.Tuple2<K,V>> foldByKey(V zeroValue, scala.Function2<V,V,V> func)
zeroValue
- (undocumented)func
- (undocumented)public RDD<scala.Tuple2<K,V>> sampleByKey(boolean withReplacement, scala.collection.Map<K,Object> fractions, long seed)
Create a sample of this RDD using variable sampling rates for different keys as specified by
fractions
, a key to sampling rate map, via simple random sampling with one pass over the
RDD, to produce a sample of size that's approximately equal to the sum of
math.ceil(numItems * samplingRate) over all key values.
withReplacement
- whether to sample with or without replacementfractions
- map of specific keys to sampling ratesseed
- seed for the random number generatorpublic RDD<scala.Tuple2<K,V>> sampleByKeyExact(boolean withReplacement, scala.collection.Map<K,Object> fractions, long seed)
This method differs from sampleByKey
in that we make additional passes over the RDD to
create a sample size that's exactly equal to the sum of math.ceil(numItems * samplingRate)
over all key values with a 99.99% confidence. When sampling without replacement, we need one
additional pass over the RDD to guarantee sample size; when sampling with replacement, we need
two additional passes.
withReplacement
- whether to sample with or without replacementfractions
- map of specific keys to sampling ratesseed
- seed for the random number generatorpublic RDD<scala.Tuple2<K,V>> reduceByKey(Partitioner partitioner, scala.Function2<V,V,V> func)
partitioner
- (undocumented)func
- (undocumented)public RDD<scala.Tuple2<K,V>> reduceByKey(scala.Function2<V,V,V> func, int numPartitions)
func
- (undocumented)numPartitions
- (undocumented)public RDD<scala.Tuple2<K,V>> reduceByKey(scala.Function2<V,V,V> func)
func
- (undocumented)public scala.collection.Map<K,V> reduceByKeyLocally(scala.Function2<V,V,V> func)
func
- (undocumented)public scala.collection.Map<K,Object> countByKey()
public PartialResult<scala.collection.Map<K,BoundedDouble>> countByKeyApprox(long timeout, double confidence)
The confidence is the probability that the error bounds of the result will contain the true value. That is, if countApprox were called repeatedly with confidence 0.9, we would expect 90% of the results to contain the true count. The confidence must be in the range [0,1] or an exception will be thrown.
timeout
- maximum time to wait for the job, in millisecondsconfidence
- the desired statistical confidence in the resultpublic RDD<scala.Tuple2<K,Object>> countApproxDistinctByKey(int p, int sp, Partitioner partitioner)
The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available here.
The relative accuracy is approximately 1.054 / sqrt(2^p)
. Setting a nonzero (sp
is
greater than p
) would trigger sparse representation of registers, which may reduce the
memory consumption and increase accuracy when the cardinality is small.
p
- The precision value for the normal set.
p
must be a value between 4 and sp
if sp
is not zero (32 max).sp
- The precision value for the sparse set, between 0 and 32.
If sp
equals 0, the sparse representation is skipped.partitioner
- Partitioner to use for the resulting RDD.public RDD<scala.Tuple2<K,Object>> countApproxDistinctByKey(double relativeSD, Partitioner partitioner)
The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available here.
relativeSD
- Relative accuracy. Smaller values create counters that require more space.
It must be greater than 0.000017.partitioner
- partitioner of the resulting RDDpublic RDD<scala.Tuple2<K,Object>> countApproxDistinctByKey(double relativeSD, int numPartitions)
The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available here.
relativeSD
- Relative accuracy. Smaller values create counters that require more space.
It must be greater than 0.000017.numPartitions
- number of partitions of the resulting RDDpublic RDD<scala.Tuple2<K,Object>> countApproxDistinctByKey(double relativeSD)
The algorithm used is based on streamlib's implementation of "HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm", available here.
relativeSD
- Relative accuracy. Smaller values create counters that require more space.
It must be greater than 0.000017.public RDD<scala.Tuple2<K,scala.collection.Iterable<V>>> groupByKey(Partitioner partitioner)
partitioner
- (undocumented)PairRDDFunctions.aggregateByKey
or PairRDDFunctions.reduceByKey
will provide much better performance.
, As currently implemented, groupByKey must be able to hold all the key-value pairs for any
key in memory. If a key has too many values, it can result in an OutOfMemoryError
.
public RDD<scala.Tuple2<K,scala.collection.Iterable<V>>> groupByKey(int numPartitions)
numPartitions
partitions. The ordering of elements within
each group is not guaranteed, and may even differ each time the resulting RDD is evaluated.
numPartitions
- (undocumented)PairRDDFunctions.aggregateByKey
or PairRDDFunctions.reduceByKey
will provide much better performance.
, As currently implemented, groupByKey must be able to hold all the key-value pairs for any
key in memory. If a key has too many values, it can result in an OutOfMemoryError
.
public RDD<scala.Tuple2<K,V>> partitionBy(Partitioner partitioner)
partitioner
- (undocumented)public <W> RDD<scala.Tuple2<K,scala.Tuple2<V,W>>> join(RDD<scala.Tuple2<K,W>> other, Partitioner partitioner)
this
and other
. Each
pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in this
and
(k, v2) is in other
. Uses the given Partitioner to partition the output RDD.other
- (undocumented)partitioner
- (undocumented)public <W> RDD<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>> leftOuterJoin(RDD<scala.Tuple2<K,W>> other, Partitioner partitioner)
this
and other
. For each element (k, v) in this
, the
resulting RDD will either contain all pairs (k, (v, Some(w))) for w in other
, or the
pair (k, (v, None)) if no elements in other
have key k. Uses the given Partitioner to
partition the output RDD.other
- (undocumented)partitioner
- (undocumented)public <W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>> rightOuterJoin(RDD<scala.Tuple2<K,W>> other, Partitioner partitioner)
this
and other
. For each element (k, w) in other
, the
resulting RDD will either contain all pairs (k, (Some(v), w)) for v in this
, or the
pair (k, (None, w)) if no elements in this
have key k. Uses the given Partitioner to
partition the output RDD.other
- (undocumented)partitioner
- (undocumented)public <W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,scala.Option<W>>>> fullOuterJoin(RDD<scala.Tuple2<K,W>> other, Partitioner partitioner)
this
and other
. For each element (k, v) in this
, the
resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other
, or
the pair (k, (Some(v), None)) if no elements in other
have key k. Similarly, for each
element (k, w) in other
, the resulting RDD will either contain all pairs
(k, (Some(v), Some(w))) for v in this
, or the pair (k, (None, Some(w))) if no elements
in this
have key k. Uses the given Partitioner to partition the output RDD.other
- (undocumented)partitioner
- (undocumented)public <C> RDD<scala.Tuple2<K,C>> combineByKey(scala.Function1<V,C> createCombiner, scala.Function2<C,V,C> mergeValue, scala.Function2<C,C,C> mergeCombiners)
createCombiner
- (undocumented)mergeValue
- (undocumented)mergeCombiners
- (undocumented)combineByKeyWithClassTag
public <C> RDD<scala.Tuple2<K,C>> combineByKeyWithClassTag(scala.Function1<V,C> createCombiner, scala.Function2<C,V,C> mergeValue, scala.Function2<C,C,C> mergeCombiners, scala.reflect.ClassTag<C> ct)
createCombiner
- (undocumented)mergeValue
- (undocumented)mergeCombiners
- (undocumented)ct
- (undocumented)public RDD<scala.Tuple2<K,scala.collection.Iterable<V>>> groupByKey()
PairRDDFunctions.aggregateByKey
or PairRDDFunctions.reduceByKey
will provide much better performance.public <W> RDD<scala.Tuple2<K,scala.Tuple2<V,W>>> join(RDD<scala.Tuple2<K,W>> other)
this
and other
. Each
pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in this
and
(k, v2) is in other
. Performs a hash join across the cluster.other
- (undocumented)public <W> RDD<scala.Tuple2<K,scala.Tuple2<V,W>>> join(RDD<scala.Tuple2<K,W>> other, int numPartitions)
this
and other
. Each
pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in this
and
(k, v2) is in other
. Performs a hash join across the cluster.other
- (undocumented)numPartitions
- (undocumented)public <W> RDD<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>> leftOuterJoin(RDD<scala.Tuple2<K,W>> other)
this
and other
. For each element (k, v) in this
, the
resulting RDD will either contain all pairs (k, (v, Some(w))) for w in other
, or the
pair (k, (v, None)) if no elements in other
have key k. Hash-partitions the output
using the existing partitioner/parallelism level.other
- (undocumented)public <W> RDD<scala.Tuple2<K,scala.Tuple2<V,scala.Option<W>>>> leftOuterJoin(RDD<scala.Tuple2<K,W>> other, int numPartitions)
this
and other
. For each element (k, v) in this
, the
resulting RDD will either contain all pairs (k, (v, Some(w))) for w in other
, or the
pair (k, (v, None)) if no elements in other
have key k. Hash-partitions the output
into numPartitions
partitions.other
- (undocumented)numPartitions
- (undocumented)public <W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>> rightOuterJoin(RDD<scala.Tuple2<K,W>> other)
this
and other
. For each element (k, w) in other
, the
resulting RDD will either contain all pairs (k, (Some(v), w)) for v in this
, or the
pair (k, (None, w)) if no elements in this
have key k. Hash-partitions the resulting
RDD using the existing partitioner/parallelism level.other
- (undocumented)public <W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,W>>> rightOuterJoin(RDD<scala.Tuple2<K,W>> other, int numPartitions)
this
and other
. For each element (k, w) in other
, the
resulting RDD will either contain all pairs (k, (Some(v), w)) for v in this
, or the
pair (k, (None, w)) if no elements in this
have key k. Hash-partitions the resulting
RDD into the given number of partitions.other
- (undocumented)numPartitions
- (undocumented)public <W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,scala.Option<W>>>> fullOuterJoin(RDD<scala.Tuple2<K,W>> other)
this
and other
. For each element (k, v) in this
, the
resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other
, or
the pair (k, (Some(v), None)) if no elements in other
have key k. Similarly, for each
element (k, w) in other
, the resulting RDD will either contain all pairs
(k, (Some(v), Some(w))) for v in this
, or the pair (k, (None, Some(w))) if no elements
in this
have key k. Hash-partitions the resulting RDD using the existing partitioner/
parallelism level.other
- (undocumented)public <W> RDD<scala.Tuple2<K,scala.Tuple2<scala.Option<V>,scala.Option<W>>>> fullOuterJoin(RDD<scala.Tuple2<K,W>> other, int numPartitions)
this
and other
. For each element (k, v) in this
, the
resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in other
, or
the pair (k, (Some(v), None)) if no elements in other
have key k. Similarly, for each
element (k, w) in other
, the resulting RDD will either contain all pairs
(k, (Some(v), Some(w))) for v in this
, or the pair (k, (None, Some(w))) if no elements
in this
have key k. Hash-partitions the resulting RDD into the given number of partitions.other
- (undocumented)numPartitions
- (undocumented)public scala.collection.Map<K,V> collectAsMap()
Warning: this doesn't return a multimap (so if you have multiple values to the same key, only one value per key is preserved in the map returned)
public <U> RDD<scala.Tuple2<K,U>> mapValues(scala.Function1<V,U> f)
f
- (undocumented)public <U> RDD<scala.Tuple2<K,U>> flatMapValues(scala.Function1<V,scala.collection.TraversableOnce<U>> f)
f
- (undocumented)public <W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>> cogroup(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2, RDD<scala.Tuple2<K,W3>> other3, Partitioner partitioner)
this
or other1
or other2
or other3
,
return a resulting RDD that contains a tuple with the list of values
for that key in this
, other1
, other2
and other3
.other1
- (undocumented)other2
- (undocumented)other3
- (undocumented)partitioner
- (undocumented)public <W> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>> cogroup(RDD<scala.Tuple2<K,W>> other, Partitioner partitioner)
this
or other
, return a resulting RDD that contains a tuple with the
list of values for that key in this
as well as other
.other
- (undocumented)partitioner
- (undocumented)public <W1,W2> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>> cogroup(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2, Partitioner partitioner)
this
or other1
or other2
, return a resulting RDD that contains a
tuple with the list of values for that key in this
, other1
and other2
.other1
- (undocumented)other2
- (undocumented)partitioner
- (undocumented)public <W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>> cogroup(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2, RDD<scala.Tuple2<K,W3>> other3)
this
or other1
or other2
or other3
,
return a resulting RDD that contains a tuple with the list of values
for that key in this
, other1
, other2
and other3
.other1
- (undocumented)other2
- (undocumented)other3
- (undocumented)public <W> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>> cogroup(RDD<scala.Tuple2<K,W>> other)
this
or other
, return a resulting RDD that contains a tuple with the
list of values for that key in this
as well as other
.other
- (undocumented)public <W1,W2> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>> cogroup(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2)
this
or other1
or other2
, return a resulting RDD that contains a
tuple with the list of values for that key in this
, other1
and other2
.other1
- (undocumented)other2
- (undocumented)public <W> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>> cogroup(RDD<scala.Tuple2<K,W>> other, int numPartitions)
this
or other
, return a resulting RDD that contains a tuple with the
list of values for that key in this
as well as other
.other
- (undocumented)numPartitions
- (undocumented)public <W1,W2> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>> cogroup(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2, int numPartitions)
this
or other1
or other2
, return a resulting RDD that contains a
tuple with the list of values for that key in this
, other1
and other2
.other1
- (undocumented)other2
- (undocumented)numPartitions
- (undocumented)public <W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>> cogroup(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2, RDD<scala.Tuple2<K,W3>> other3, int numPartitions)
this
or other1
or other2
or other3
,
return a resulting RDD that contains a tuple with the list of values
for that key in this
, other1
, other2
and other3
.other1
- (undocumented)other2
- (undocumented)other3
- (undocumented)numPartitions
- (undocumented)public <W> RDD<scala.Tuple2<K,scala.Tuple2<scala.collection.Iterable<V>,scala.collection.Iterable<W>>>> groupWith(RDD<scala.Tuple2<K,W>> other)
public <W1,W2> RDD<scala.Tuple2<K,scala.Tuple3<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>>>> groupWith(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2)
public <W1,W2,W3> RDD<scala.Tuple2<K,scala.Tuple4<scala.collection.Iterable<V>,scala.collection.Iterable<W1>,scala.collection.Iterable<W2>,scala.collection.Iterable<W3>>>> groupWith(RDD<scala.Tuple2<K,W1>> other1, RDD<scala.Tuple2<K,W2>> other2, RDD<scala.Tuple2<K,W3>> other3)
public <W> RDD<scala.Tuple2<K,V>> subtractByKey(RDD<scala.Tuple2<K,W>> other, scala.reflect.ClassTag<W> evidence$4)
this
whose keys are not in other
.
Uses this
partitioner/partition size, because even if other
is huge, the resulting
RDD will be less than or equal to us.
other
- (undocumented)evidence$4
- (undocumented)public <W> RDD<scala.Tuple2<K,V>> subtractByKey(RDD<scala.Tuple2<K,W>> other, int numPartitions, scala.reflect.ClassTag<W> evidence$5)
this
whose keys are not in other
.other
- (undocumented)numPartitions
- (undocumented)evidence$5
- (undocumented)public <W> RDD<scala.Tuple2<K,V>> subtractByKey(RDD<scala.Tuple2<K,W>> other, Partitioner p, scala.reflect.ClassTag<W> evidence$6)
this
whose keys are not in other
.other
- (undocumented)p
- (undocumented)evidence$6
- (undocumented)public scala.collection.Seq<V> lookup(K key)
key
. This operation is done efficiently if the
RDD has a known partitioner by only searching the partition that the key maps to.key
- (undocumented)public <F extends org.apache.hadoop.mapred.OutputFormat<K,V>> void saveAsHadoopFile(String path, scala.reflect.ClassTag<F> fm)
OutputFormat
class
supporting the key and value types K and V in this RDD.path
- (undocumented)fm
- (undocumented)public <F extends org.apache.hadoop.mapred.OutputFormat<K,V>> void saveAsHadoopFile(String path, Class<? extends org.apache.hadoop.io.compress.CompressionCodec> codec, scala.reflect.ClassTag<F> fm)
OutputFormat
class
supporting the key and value types K and V in this RDD. Compress the result with the
supplied codec.path
- (undocumented)codec
- (undocumented)fm
- (undocumented)public <F extends org.apache.hadoop.mapreduce.OutputFormat<K,V>> void saveAsNewAPIHadoopFile(String path, scala.reflect.ClassTag<F> fm)
OutputFormat
(mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD.path
- (undocumented)fm
- (undocumented)public void saveAsNewAPIHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<? extends org.apache.hadoop.mapreduce.OutputFormat<?,?>> outputFormatClass, org.apache.hadoop.conf.Configuration conf)
OutputFormat
(mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD.path
- (undocumented)keyClass
- (undocumented)valueClass
- (undocumented)outputFormatClass
- (undocumented)conf
- (undocumented)public void saveAsHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<? extends org.apache.hadoop.mapred.OutputFormat<?,?>> outputFormatClass, Class<? extends org.apache.hadoop.io.compress.CompressionCodec> codec)
OutputFormat
class
supporting the key and value types K and V in this RDD. Compress with the supplied codec.path
- (undocumented)keyClass
- (undocumented)valueClass
- (undocumented)outputFormatClass
- (undocumented)codec
- (undocumented)public void saveAsHadoopFile(String path, Class<?> keyClass, Class<?> valueClass, Class<? extends org.apache.hadoop.mapred.OutputFormat<?,?>> outputFormatClass, org.apache.hadoop.mapred.JobConf conf, scala.Option<Class<? extends org.apache.hadoop.io.compress.CompressionCodec>> codec)
OutputFormat
class
supporting the key and value types K and V in this RDD.
path
- (undocumented)keyClass
- (undocumented)valueClass
- (undocumented)outputFormatClass
- (undocumented)conf
- (undocumented)codec
- (undocumented)public void saveAsNewAPIHadoopDataset(org.apache.hadoop.conf.Configuration conf)
conf
- (undocumented)public void saveAsHadoopDataset(org.apache.hadoop.mapred.JobConf conf)
conf
- (undocumented)


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--- layout: default title: Quick Tournament #51 - DDraceNetwork head: menu: |
---This tournament is played on Saturday, 2020-08-30 at 18:00 CEST.
by SickCunt & Feuerfaust Ace
Difficulty: ★✰✰✰✰, Points: 34
6 tees finished (average time: 71:06)
2 teams finished (biggest team: 4)


Monroe gives Thanksgiving turkeys to community leaders
by Tyler WannMONROE, La. (KNOE) The City of Monroe gave away Thanksgiving Turkeys to groups that have helped out the community early Friday.
Cars lined up around the Civic Center as fire and police departments helped the mayor and event sponsors give out the turkeys.
They were given to representatives from local soup kitchens, senior citizen groups, churches, housing authority sites, and non-profits, among others.
Monroe Mayor Jamie Mayo said it was a small way to give back to the community.
"This is a time to give back because we're thankful to God for all of his blessings," he said. "There's so many individuals who are in need of so many different things and this is just a small piece of what we do to be able to give back to the community."


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ocular by Jeffalo - Used to get ocular statuses for the List of Forum Helpers Page.
ScratchDB by DatOneLefty - Used to get post counts for the List of Forum Helpers Page.
Scratch Sprite Library - Taco Wizard Sprite used on the 404 page. Scratch is a project of the Scratch Foundation, in collaboration with the Lifelong Kindergarten Group at the MIT Media Lab. It is available for free at https://scratch.mit.edu/


InetSocketAddress address
String keyspace
String table
InetSocketAddress address
InetSocketAddress address
InetSocketAddress address
InetSocketAddress address
InetSocketAddress address
InetSocketAddress address
InetSocketAddress address
InetSocketAddress address
InetSocketAddress address
ConsistencyLevel consistency
int received
int required
int failed
boolean dataPresent
boolean dataPresent
InetSocketAddress address
InetSocketAddress address
InetSocketAddress address
InetSocketAddress address
InetSocketAddress address
ConsistencyLevel consistency
int required
int alive
InetSocketAddress address
ProtocolVersion currentVersion
InetSocketAddress address
ProtocolVersion unsupportedVersion
ProtocolVersion serverVersion
WriteType writeType
int failed
WriteType writeType


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{% extends "base.html" %} {% load lookup %} {% block content %}
No blog entries are available. How weird.


Enables the ability to browse workspaces of jobs being built using docker containers.
Specify the location containing the workspace folder (e.g. /home/jenkins) on the Jenkins master where the job workspaces will be mapped from the images using volumes, network shares, etc.
Changes to this path will be applied after the next build run. All builds must have been done on docker containers, delete any builds run on normal slaves.


Bookme is an online collaborative marketplace and management service, enabling event hosts to book and manage performing artists.
Find Out MoreBookme provides a convenient, collaborative marketplace to find, hire and manage artists and upcoming events.
Bookme allows for a unique way to promote your brand, reach out to a large audience, find new clients or events to perform at, manage upcoming shows and get paid in full, securely and on time.
With Bookme, you’ll be able to find the perfect artist for your concert, show, or any live event
We make the booking process simple: after a request is sent to an artist and they approve the artist is reserved
Say goodbye to the headache of artist logistics! You’ll be able to work through every aspect of the booking process through Bookme
With our secure payment processor, both you and the artist will be able to rest easy knowing your money is 100% secure
Find amazing event opportunities: Thinking of Doing in Ibiza? Signing in San Francisco? Now it’s easier than ever to find gigs that interest you
Bookme does the heavy lifting regarding dealing with event contracts and logistics so you can spend more time performing
No more stressing that the host won’t pay you in full or worrying if transportation is organized. You’ll be able to track exactly what the host has left to fulfill for the event
Every event is protected by “Bookme Payment Protection” - ensuring that you will get paid for your performance
Bookme is currently being developed. For all inquiries about the service, please email


I like to make BlockLauncher addons. Hint: I'm not good at them.
Be happy I didn't cover these links in 3 adf.ly links.


This week, we are working on last two rooms: Wendy's room and Sonia's room.
What Every Member Did
- Jack: Implemented a stand alone dynamic loading component
- Zhifu: futher optimized the models in Jane's room and added colliders to Sonia's room and Wendy's room
- Diana: find and decimate to optimize models used in Sonia's room and Wendy's room. Done all the positioning, also update the colliders in meeting room, Lucas's room and Peter's room.
- Sean:
Room Tour
Update on Code
Diana: I edited *index.html* to add in the furnitures of Sonia's and Wendy's rooms. Colliders are added.
Zhifu: edited index.html to add models and define-collider.js to add colliders.
Jack: Implemented dynamic loading component
Sean: Fixed various bugs in the voting system. Working with Jack to put code together.
Update on Ideas
Not a lot of change of ideas. Stay tuned!
Plan for next week
Milestone: enhance game mechanics, combine the dynamic loading into the scene, also update all the scripts.
Description: enhance the game mechanics, we also need to incorporate dynamic loading into the room layout, and scripts need to be added in the script panel as well.
All the settings and environments are set, so we need to make sure the flow from group discussion to room examination is clear and easy to understand. The timing system, an introduction, a voting system has been implemented, but can be refined.
This is the refinement stage, where we need to make sure everything from introduction to group discussion to room examination to voting to ending are clear and in good state. Some user testing can be done with friends and improve based on the feedback.
- Jack: integrate dynamic loading into the project and test bugs
- Zhifu: add colliders to Jane's room and work with Diana to write up scripts if needed.
- Diana: write up and refind the scripts for all the interactable items and player's storyline.
- Sean: Wrap up the voting system. Work with Jack to clean up and put the code together
Blockers
N/A


Call external program to take photos. To use Raspberry Pi official camera you can use command below. raspistill --output {{file}} --nopreview


The Hydrolysis of Some N-Substituted Cyclic Phosphorodiamidates
of Ethylenediamine
A thesis presented by Lewis David Williams to The Department of Chemistry in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of
Chemistry Harvard University
Cambridge, Massachusetts
October, 1970
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import config.FrontendAppConfig @import views.CaseDetailTab.CaseDetailTab @(appConfig: FrontendAppConfig, activeTab : CaseDetailTab, viewTable : Html)(implicit request: Request[_], messages: Messages) @main_template( title = messages("index.title"), appConfig = appConfig, contentClass = Some("column-full"), bodyClasses = None) { @components.heading(messages("index.heading")) @components.button_link(messages("site.start"), routes.BeforeYouStartController.onPageLoad.url, id = Some("start-application"))


Luvit 2.5.6 Documentation
Table of Contents
- Datagram | UDP
- dgram.createSocket(type, callback)
- Class: dgram.Socket
- Socket:recvStart()
- Socket:recvStop()
- Socket:setTimeout(msecs, callback)
- Socket:send(data, port, host, callback)
- Socket:bind(port, host, options)
- Socket:close(callback)
- Socket:address()
- Socket:setBroadcast(status)
- Socket:setMembership(multicastAddress[, multicastInterface], op)
- Socket:addMembership(multicastAddress[, interfaceAddress])
- Socket:dropMembership(multicastAddress[, interfaceAddress])
- Socket:setTTL(ttl)
Datagram | UDP#
Node-style udp module for luvit
local dgram = require('dgram')
dgram.createSocket(type, callback)#
Creates a new datagram socket Callback is triggered every time the 'message' event gets emitted
Class: dgram.Socket#
Socket extends Emitter and inherits all the events thereof. The dgram Socket class encapsulates the datagram functionality. It should be created via dgram.createSocket(...)
Socket:recvStart()#
Start receiving on socket
Socket:recvStop()#
Stop listening on socket
Socket:setTimeout(msecs, callback)#
Sets a socket timeout
Socket:send(data, port, host, callback)#
Sends data down the udp socket
Socket:bind(port, host, options)#
Starts listening on the specified port and host.
Socket:close(callback)#
Closes a socket instance and fires the callback after cleanup
Socket:address()#
Returns an object containing the address information for a socket. For UDP sockets, this object will contain address , family and port.
Socket:setBroadcast(status)#
- status - Boolean Sets or clears the SO_BROADCAST socket option. When this option is set, UDP packets may be sent to a local interface's broadcast address.
Socket:setMembership(multicastAddress[, multicastInterface], op)#
Sets membership status for a multicast group. Op can be 'join' or 'leave'.
Socket:addMembership(multicastAddress[, interfaceAddress])#
- multicastAddress String
- multicastInterface String, Optional Tells the kernel to join a multicast group with IP_ADD_MEMBERSHIP socket option.
If multicastInterface is not specified, the OS will try to add membership to all valid interfaces.
Socket:dropMembership(multicastAddress[, interfaceAddress])#
- multicastAddress String
- multicastInterface String, Optional Opposite of addMembership - tells the kernel to leave a multicast group with IP_DROP_MEMBERSHIP socket option. This is automatically called by the kernel when the socket is closed or process terminates, so most apps will never need to call this.
If multicastInterface is not specified, the OS will try to drop membership to all valid interfaces.
Socket:setTTL(ttl)#
- ttl Integer Sets the IP_TTL socket option. TTL stands for "Time to Live," but in this context it specifies the number of IP hops that a packet is allowed to go through. Each router or gateway that forwards a packet decrements the TTL. If the TTL is decremented to 0 by a router, it will not be forwarded. Changing TTL values is typically done for network probes or when multicasting.
The argument to setTTL() is a number of hops between 1 and 255. The default on most systems is 64.


001/** 002 * Licensed to the Apache Software Foundation (ASF) under one 003 * or more contributor license agreements. See the NOTICE file 004 * distributed with this work for additional information 005 * regarding copyright ownership. The ASF licenses this file 006 * to you under the Apache License, Version 2.0 (the 007 * "License"); you may not use this file except in compliance 008 * with the License. You may obtain a copy of the License at 009 * 010 * http://www.apache.org/licenses/LICENSE-2.0 011 * 012 * Unless required by applicable law or agreed to in writing, software 013 * distributed under the License is distributed on an "AS IS" BASIS, 014 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 015 * See the License for the specific language governing permissions and 016 * limitations under the License. 017 */ 018 019package org.apache.hadoop.filecache; 020 021import org.apache.hadoop.classification.InterfaceAudience; 022import org.apache.hadoop.classification.InterfaceStability; 023import org.apache.hadoop.conf.Configuration; 024import org.apache.hadoop.fs.FileSystem; 025import org.apache.hadoop.fs.Path; 026import org.apache.hadoop.mapreduce.Job; 027 028/** 029 * Distribute application-specific large, read-only files efficiently. 030 * 031 *
DistributedCache is a facility provided by the Map-Reduce 032 * framework to cache files (text, archives, jars etc.) needed by applications. 033 *
 034 * 035 *
Applications specify the files, via urls (hdfs:// or http://) to be cached 036 * via the {@link org.apache.hadoop.mapred.JobConf}. The 037 * DistributedCache assumes that the files specified via urls are 038 * already present on the {@link FileSystem} at the path specified by the url 039 * and are accessible by every machine in the cluster.
 040 * 041 *
The framework will copy the necessary files on to the slave node before 042 * any tasks for the job are executed on that node. Its efficiency stems from 043 * the fact that the files are only copied once per job and the ability to 044 * cache archives which are un-archived on the slaves.
 045 * 046 *
DistributedCache can be used to distribute simple, read-only 047 * data/text files and/or more complex types such as archives, jars etc. 048 * Archives (zip, tar and tgz/tar.gz files) are un-archived at the slave nodes. 049 * Jars may be optionally added to the classpath of the tasks, a rudimentary 050 * software distribution mechanism. Files have execution permissions. 051 * In older version of Hadoop Map/Reduce users could optionally ask for symlinks 052 * to be created in the working directory of the child task. In the current 053 * version symlinks are always created. If the URL does not have a fragment 054 * the name of the file or directory will be used. If multiple files or 055 * directories map to the same link name, the last one added, will be used. All 056 * others will not even be downloaded.
 057 * 058 *
DistributedCache tracks modification timestamps of the cache 059 * files. Clearly the cache files should not be modified by the application 060 * or externally while the job is executing.
 061 * 062 *
Here is an illustrative example on how to use the 063 * DistributedCache:
 064 *

 065 * // Setting up the cache for the application 066 * 067 * 1. Copy the requisite files to the FileSystem: 068 * 069 * $ bin/hadoop fs -copyFromLocal lookup.dat /myapp/lookup.dat 070 * $ bin/hadoop fs -copyFromLocal map.zip /myapp/map.zip 071 * $ bin/hadoop fs -copyFromLocal mylib.jar /myapp/mylib.jar 072 * $ bin/hadoop fs -copyFromLocal mytar.tar /myapp/mytar.tar 073 * $ bin/hadoop fs -copyFromLocal mytgz.tgz /myapp/mytgz.tgz 074 * $ bin/hadoop fs -copyFromLocal mytargz.tar.gz /myapp/mytargz.tar.gz 075 * 076 * 2. Setup the application's JobConf: 077 * 078 * JobConf job = new JobConf(); 079 * DistributedCache.addCacheFile(new URI("/myapp/lookup.dat#lookup.dat"), 080 * job); 081 * DistributedCache.addCacheArchive(new URI("/myapp/map.zip", job); 082 * DistributedCache.addFileToClassPath(new Path("/myapp/mylib.jar"), job); 083 * DistributedCache.addCacheArchive(new URI("/myapp/mytar.tar", job); 084 * DistributedCache.addCacheArchive(new URI("/myapp/mytgz.tgz", job); 085 * DistributedCache.addCacheArchive(new URI("/myapp/mytargz.tar.gz", job); 086 * 087 * 3. Use the cached files in the {@link org.apache.hadoop.mapred.Mapper} 088 * or {@link org.apache.hadoop.mapred.Reducer}: 089 * 090 * public static class MapClass extends MapReduceBase 091 * implements Mapper<K, V, K, V> { 092 * 093 * private Path[] localArchives; 094 * private Path[] localFiles; 095 * 096 * public void configure(JobConf job) { 097 * // Get the cached archives/files 098 * File f = new File("./map.zip/some/file/in/zip.txt"); 099 * } 100 * 101 * public void map(K key, V value, 102 * OutputCollector<K, V> output, Reporter reporter) 103 * throws IOException { 104 * // Use data from the cached archives/files here 105 * // ... 106 * // ... 107 * output.collect(k, v); 108 * } 109 * } 110 * 111 *

 112 * 113 * It is also very common to use the DistributedCache by using 114 * {@link org.apache.hadoop.util.GenericOptionsParser}. 115 * 116 * This class includes methods that should be used by users 117 * (specifically those mentioned in the example above, as well 118 * as {@link DistributedCache#addArchiveToClassPath(Path, Configuration)}), 119 * as well as methods intended for use by the MapReduce framework 120 * (e.g., {@link org.apache.hadoop.mapred.JobClient}). 121 * 122 * @see org.apache.hadoop.mapred.JobConf 123 * @see org.apache.hadoop.mapred.JobClient 124 * @see org.apache.hadoop.mapreduce.Job 125 */<EMAIL_ADDRESS><EMAIL_ADDRESS>128public class DistributedCache extends 129 org.apache.hadoop.mapreduce.filecache.DistributedCache { 130 // 131}


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PLANET SICK-BOY: http://www.sick-boy.com
Antonio Banderas' directorial debut is surprisingly solid as he assembles an eclectic cast in an adaptation of Mark Childress' novel of the same name. Banderas has wisely chosen material that isn't autobiographical for his first feature, eliminating concern that Crazy in Alabama would be like the work of other actor-turned-directors, most recently Frank Whaley's Joe the King. Alabama features two odd plots that are tied together with a big courtroom finale and one central character.
Set in the summer of 1965, Alabama is narrated by Peejoe (Lucas Black, Slingblade) – short for Peter Joseph – a young boy in a small town that still hasn't been notified of the Emancipation Proclamation. As the film opens, his oddball Aunt Lucille (Melanie Griffith, Another Day in Paradise) tells Peejoe that she has killed her husband of thirteen years and father of her seven children.  She is an aspiring actress who dreams of landing a gig on Bewitched and heads for Los Angeles to pursue her unlikely goal.
Peejoe, who had previously lived with his Aunt Lucille, is shipped off to live with his Uncle Dove (David Morse, The Negotiator), a soft-spoken funeral director. As summer heats up, so does the tension in the still racially segregated small town. Peejoe witnesses the murder of a black youth that had merely used the white-only pool facilities. The fact that the murder was committed by Sheriff John Doggett (Meat Loaf, Fight Club) inspires Peejoe to speak out publicly about racial injustice. Black couldn't look more like a hillbilly, but Peejoe has a perpetual look of surprise when he watches the actions of his community.
Although the film seems a little choppy at first because of the transition between the racism and Lucille plots, the picture is very well paced.  The big courtroom finale that ties the two together is pretty well done, with only Griffith's weepy monologue seeming a bit too much. The judge is played by Rod Steiger, who was either drunk or had never seen his lines before. Another possibility for his odd acting was that Banderas filmed a rehearsal without Stieger's knowledge.
Griffiths does quite a decent job tackling the role of Lucille. With her long legs, black hair, big red lips and blue eyes she looks like she could be the mother of Angelina Jolie and Charlize Theron, if they were sisters (and wouldn't you have loved to live next door to that family). Her Lucille is plagued by the voice of her decapitated husband throughout the film, most likely on account of carrying his head around in a hat box the entire time. Cathy Moriarty (Gloria), Richard Schiff (The West Wing), John Beasley (The General's Daughter), Robert Wagner (Austin Powers) and Noah Emmerich (The Truman Show) round out this bizarre cast.
1:55 - PG-13 for violence, thematic material, language and adult situations
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Company profile - Canadian Company Capabilities
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
JJM and Associates
Mailing Address:
509-1030 Pearson Dr.OAKVILLE, Ontario
L6H 2A8
Location Address:
509-1030 Pearson Dr.OAKVILLE, Ontario
L6H 2A8
Tel.:<PHONE_NUMBER>
Fax:
Logo
Company Profile
An executive coaching and consulting business with cross industry cross cultural expertise. Specialties: leadership development, talent management, career transition, virtual group facilitation and small business planning. Clients range from solopreneurs to executives within the Fortune 500.
Full profile
Full profile
Contact information
Company description
541619 - Other Management Consulting Services
Product / Service / Licensing
- 360 degree feedback assessment with follow-on coaching
- Relationship management
- Career Advancement
Market profile
Industry sector market interests:
- Consumer Products
- Information Technology and Telecommunications
- Manufacturing
- Service Industry
Sector information
Unique applications:
We provide strategies, tools and techniques to identify and develop adult potential. We work with companies as well as private clients who are dissatisfied with their current results and are ready to make a change. Unlike most solutions offered, we include follow-on coaching work during implementation to help overcome obstacles and accelerate results.Key / Major clients:
Cllients span 14 countries and range from solo-preneurs to executives within the Fortune 500.Testimonial:
Jackie Martin very well applies her experience in the field to connect theoretical concepts with practical experience. She acted as a very good sounding board and just intervened if she could add anything to the discussion. This approach very much helped me to rephrase the issues on hand using my own words and to also develop my own conclusions and action plan. Sr. Executive, Financial Institution"I needed some guidance as to how to organize my time, I wished to make my plan for the future and I needed some guidance and reassurance regarding my sales approaches.
I appreciate very much that you tailored your program to my needs. You showed me how I was approaching my job, then helped me clarify and prioritize my daily activities. For my future goal, you had me create a path and timeline to achieve it. I know now that there is a way to reach my objectives. Thank you for getting me past this troubled spot." D. Vaninni, Sales Manager
"Having worked with Jackie for the last four years I have found her to be an excellent coach. She has the ability to help her clients identify the most important issues they are facing and help them work to a solution. Her experience in selling has given her a deep understanding of how business works and she uses that to reinforce the work she does. Jackie has a wonderful outlook on the world and her curiosity helps her clients delve into unexplored territory." W. Lewis, Wayne Lewis and Associates
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Contacts
Contact information
Description
Company description
541619 - Other Management Consulting Services
Products, services and licensing
Product / Service / Licensing
- 360 degree feedback assessment with follow-on coaching
- Relationship management
- Career Advancement
Market
Market profile
Industry sector market interests:
- Consumer Products
- Information Technology and Telecommunications
- Manufacturing
- Service Industry
Sector
Sector information
Unique applications:
We provide strategies, tools and techniques to identify and develop adult potential. We work with companies as well as private clients who are dissatisfied with their current results and are ready to make a change. Unlike most solutions offered, we include follow-on coaching work during implementation to help overcome obstacles and accelerate results.Key / Major clients:
Cllients span 14 countries and range from solo-preneurs to executives within the Fortune 500.Testimonial:
Jackie Martin very well applies her experience in the field to connect theoretical concepts with practical experience. She acted as a very good sounding board and just intervened if she could add anything to the discussion. This approach very much helped me to rephrase the issues on hand using my own words and to also develop my own conclusions and action plan. Sr. Executive, Financial Institution"I needed some guidance as to how to organize my time, I wished to make my plan for the future and I needed some guidance and reassurance regarding my sales approaches.
I appreciate very much that you tailored your program to my needs. You showed me how I was approaching my job, then helped me clarify and prioritize my daily activities. For my future goal, you had me create a path and timeline to achieve it. I know now that there is a way to reach my objectives. Thank you for getting me past this troubled spot." D. Vaninni, Sales Manager
"Having worked with Jackie for the last four years I have found her to be an excellent coach. She has the ability to help her clients identify the most important issues they are facing and help them work to a solution. Her experience in selling has given her a deep understanding of how business works and she uses that to reinforce the work she does. Jackie has a wonderful outlook on the world and her curiosity helps her clients delve into unexplored territory." W. Lewis, Wayne Lewis and Associates


Build environment API¶
-
class
sphinx.environment.
BuildEnvironment
[source]¶ Attributes
-
srcdir
¶ Source directory.
-
confdir
¶ Directory containing
conf.py
.
-
doctreedir
¶ Directory for storing pickled doctrees.
-
found_docs
¶ A set of all existing docnames.
-
metadata
¶ Dictionary mapping docnames to “metadata” (see File-wide metadata).
-
titles
¶ Dictionary mapping docnames to the docutils node for their main title.
-
docname
¶ Returns the docname of the document currently being parsed.
Utility methods
-
warn
(docname, msg, lineno=None, **kwargs)[source]¶ Emit a warning.
This differs from using
app.warn()
in that the warning may not be emitted instantly, but collected for emitting all warnings after the update of the environment.
-
doc2path
(docname, base=True, suffix=None)[source]¶ Return the filename for the document name.
If base is True, return absolute path under self.srcdir. If base is None, return relative path to self.srcdir. If base is a path string, return absolute path under that. If suffix is not None, add it instead of config.source_suffix.
-
relfn2path
(filename, docname=None)[source]¶ Return paths to a file referenced from a document, relative to documentation root and absolute.
In the input “filename”, absolute filenames are taken as relative to the source dir, while relative filenames are relative to the dir of the containing document.
-
note_dependency
(filename)[source]¶ Add filename as a dependency of the current document.
This means that the document will be rebuilt if this file changes.
filename should be absolute or relative to the source directory.
-


Error: Unauthorised Access!!!Please click here to login or contact your System Administrator for access.


Neighborhood Analysis: Spenc Clustering with Chord Diagram, San Diego
Submit for Neighborhood Analysis
Initialize all maps
Set Globally


DevOps is a cultural and professional movement, focused on how we build and operate high velocity organizations, born from the experiences of its practitioners.
Since 2009, DevOps practitioners have been gathering for locally organized conferences known as DevOps Days. We will be hosting DevOpsDays DC on June 11 and 12.
I’d like to invite you to join us for DevOps Days DC as a speaker, participant, and DevOps enthusiast. The call for presenters (CFP) is open through April 15. We are seeking presenters from the public and private sector working within 100 miles of DC who are eager to share their experiences with adopting DevOps practices in their organization. Submit your proposal today.
Registration is open now and we’re excited to welcome a representative blend of attendees for our area. If you’re an employee of the Federal Government, we have a block of tickets set aside just for you. Please register for the “Government Admission” ticket type using your .gov or .mil email address. Everyone else (including federal contractors) can register for the “General Admission” ticket type. Register now to join professionals from organizations such as the US Patent and Trademark Office (USPTO), AddThis, Wells Fargo, and National Institute of Standards and Technology (NIST) at DevOpsDays DC.
Sponsorship opportunities are also available. DevOpsDays is a self-organizing conference for DevOps practitioners that depends on your sponsorships to happen.


| Method |
__init__ |
Undocumented |
| Method |
insert |
insert an entry with key key and value value if value is not None else {'geno':key} and self[key]['kwarg'] = kwarg if kwarg is not None for the further kwargs. |
Inherited from SolutionDict:
| Method |
key |
compute key of x |
| Method |
__setitem__ |
define self[key] = value |
| Method |
__getitem__ |
define access self[key] |
| Method |
__delitem__ |
remove only most current key-entry of list with same keys |
| Method |
truncate |
delete old entries to prevent bloat |
| Static Method |
_hash |
Undocumented |
Inherited from DerivedDictBase (via SolutionDict):


Premiere seating, private lanes, huge screen TVs, handcrafted cocktails and premium beers. There’s never been a better way to spend time with friends.


I am Vasavisindhu Saladi.I am a Software Developer.Some quick example text to build on the card title and make up the bulk of the card's content.Some quick example text to build on the card title and make up the bulk of the card's content


Count the problems in this picture:
I count eight, not including my crying-out-for-a-lightning-cable battery life and the one-bar reception on Rogers.1
Eight of the top ten “Free” iPhone apps are not free at all. They contain in-app purchases. Apple generously spells it out for us—in size 8-pt. Helvetica Neue, in text a shade darker than the white background,underneath big blue buttons that scream FREE.
These warnings, new as of iOS 7.1.1, are a welcome addition to the platform. (It only took an FTC investigation, $32-million in refunds, and a media backlash.) But they don’t address the problem of freemium creep on the App Store.
Apps with in-app purchases are still too easy to mistake for their free counterparts. They’re still cluttering up parts of the storefront where they don’t belong. And they’re more popular than ever, crowding out free and paid apps alike. None of those problems can be fixed with disclaimers.
It’s time for Apple to put its foot down and tell non-free apps where they belong. It’s time they were sent to the Freemium Ghetto.
Solution 1: Merge Freemium and Paid Apps
I don’t hate the freemium business model. Some companies wield in-app purchases for evil, but plenty of small developers have foundwell-deserved success by releasing their product for free with paid upgrades.
Freemium apps can be hugely effective, and that’s all the more reason why Apple needs to move them somewhere else. They’re are just too good at what they do. Their cost of entry—zilch—is just too compelling. They bulldoze everything in their path.
But where to put them? One possibility is to group freemium apps with paid apps instead of free apps. That way, consumers could browse the Free listings without having to worry about spending a cent, and all monetized apps would compete transparently and on the same level.
Here is what the App Store might look like if freemium and paid apps shared a single “Paid” list:
This scenario would be a kick in the pants for developers of traditional paid apps. Only a handful would ever make it near the top ten when pitted against “free” competition. Freemium developers wouldn’t be so happy either. They’d have to put up with the stigma of being “Paid”. And consumers wouldn’t be any less confused by the melange of payment options than they are now. Merging lists is a bad deal for everyone.
Solution 2: The Freemium Ghetto
A better solution requires a third type of app and a third category on the “Top Charts” screen. (The existing “Top Grossing” list would have to hit the chopping board, but that metric’s almost entirely meaningless for consumers anyway.) A new list would rise up—one where freemium apps could thrive:
The Freemium Ghetto.
All apps now belong to one of three types: Free, Paid, and Demo (freemium).2
Drawing solid lines between these groups would improve the app-shopping experience immensely. In this system, it’s immediately obvious whether there’s a possibility of an app ever costing money. Gone is the constant need to question whether that free game you just impulse-downloaded has hidden costs. The App Store would feel less like the casino that it resembles today.
Better labeling for freemium apps would give paid apps more of a chance to succeed. Given the choice between an app labeled “FREE” and one that costs a buck, it’s obvious what most consumers will choose. But if the decision is between a demo and a full version, the choice is more even-handed.
The Freemium Ghetto would be good news for freemium creators too—at least in the long run. By reserving the “FREE” label for those apps that truly deserve it, Apple would build consumer trust and confidence in its platform. That’s something all developers should want to see happen.
More honesty would help developers avoid the kind of PR disasters that happen when children (or childish adults) waste hundreds of dollars on an app or game they mistakingly believed to be free. Parents would find the three-way distinction easier to understand than the existing choice between Paid, Free and “Free.”
But the best part for developers? An exclusive category for apps with IAP would do wonders for exposure. No longer would freemium apps have to compete with free apps for their positions in the ranking. In the Freemium Ghetto, the majority of freemium apps would be more visible than they are now.
So think about it, Apple: embrace the Freemium Ghetto. It might sound like kind of scary change that will make certain big name game publishers angry, but it’d be worth it. Do it for your customers. Do it for your developer community. Do it the future of the App Store.
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For Americans: Pretend AT&T and Fox Broadcasting had a Canadian baby out of wedlock. That’s Rogers. One part Big Telecom, one part Big Media. ↩
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I’m fond of the word “Demo” because of its connotations from the gaming world. Perhaps Apple could come up with a better moniker. It doesn’t matter. What matters is that there’s a clear separation between the three payment models on the App Store. ↩


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The first task at hand was to create a group compact amongst ourselves to set what we expected from one another. The intended purpose was to create guidelines for ourselves to ensure everyone is doing their portion of the project and set guidelines for possible issues we may encounter. These included thoughts on team and individual norms, project management tools and communication.
Some of the main points in the compact were:
After discussing amongst ourselves we found that the lack of transparency in company supply chains is an alarming reality which allows companies to maintain a public image of supporting social-justice concerns while simultaneously profiting off of grossly unethical incidents in their supply chain.
Members of the Uyghur ethnic minority were found working under conditions that strongly suggest forced labor in the supply chains of more than 80 well-known brands, including Apple, BMW, Nike, and Samsung [1]. A factory producing shoes for Nike had watchtowers, barbed wire fences, and police guard boxes [1]. Further, Uyghur workers were unable to go home for holidays [1]. However, media coverage has allowed attempts at solving the problem.
Oxfam surveys showed that 90% of women working on grape farms in South Africa reported not having enough to eat in the previous month [3]. They also showed that 75% of the women working on fruit or vegetable farms said that they or a family member missed meals in the previous month because they could not afford enough food [3]. In the U.S., Oxfam ranked six large retailers including Albertson, Costco, Kroger, Walmart, and Whole Foods, and found that all six need to radically improve transparency around their food sourcing policies and show that their suppliers respect labor and human rights [3].
Dysprosium and terbium mining occur in Jianxi, China. Both are essential for manufacturing laptops, smartphones, speakers, and other modern technology [4]. Only ~0.2% of mined clay contains these materials, so the remaining 99.8% is dumped into hills and streams [4]. This process produces 75,000 liters of acidic water and one ton of radioactive waste for every ton of rare earth materials [4].
Re-maximizing is a software-based automatic ethical evaluation pipeline consisting of web-scraping capabilities to identify publicly available press which hints at the ethics of an organization, domain-specific machine-learning models trained off of identifying features present in prior instances of ethical misconduct, and data visualization capabilities to succinctly and accurately display key signals of the ethics of an organization in areas such as monetary inequality, environment impact, unfair outcomes along racial lines.
The purpose of our product is to raise awareness for potentially unethical practices present within corporations and in doing so, cause pressure for change. It is very difficult to change an organization when the problems to change are not known, so we hope that our product can provide the starting point for positive change by increasing transparency. We hope to achieve this transparency both for companies about their own supply chain and for consumers about the companies they buy from. It is possible that organizations may be unaware of certain unethical practices within their supply chain, so we hope that our software can provide greater clarity here. We also hope to prevent instances where well-intentioned consumers end up inadvertently supporting unethical companies because instances of misconduct are hidden.
As a next step, we created a Work Plan to show who would be tasked with which assignments.
Our individual roles were:
Figure: Gantt Chart for work distribution
Documents were created for every aspect of our project in order to show our process and create a coherent final product where all three members are aligned in thinking and can make necessary edits.
The discussions on how to implement each document were done through Discord. There, we thought out what we each wanted for each document and came to the following conclusions shown in the following pictures:
Throughout the process, we each sent reminders to each other regarding upcoming due dates through email.
Re-Maximizing uses web scraping as one of the main means of gathering information. Re-maximizing pulls from Twitter posts via the publicly available Twitter API as well as from recent news from newsapi.org. This allows for the dynamic retrieval of structured news content surrounding a particular topic. Specifically, if we would like to find all the news surrounding a company or an event, a news API or Twitter API will allow us to search for that content. Therefore, news articles on any topic can be rapidly and reliably retrieved for our analysis. Furthermore, Re-maximizing features a web-scraper which is implemented using the Python library, Scrapy. Scrapy has a lot of benefits for web-scraping, including integration with additional libraries and rapid speed.
Re-Maximizing also uses machine learning to create the ethical ratings.
Re-maximizing performs machine learning using TensorFlow to check for markers of
potential misconduct. An example of our machine learning model development process
is provided. After collecting large quantities of press data related to companies
in ethical scandals and substantial quantities of press data from companies who have
a clean record, the “unethical” pieces of textual web content and “ethical” pieces of
textual web content are placed in .csv (comma-separated values) files with the appropriate
label (“ethical” or “unethical”). To train a particular machine learning model, a CSV
file is split into a “training group” representing 75% of the data and a “testing group”
representing 25% of the data in the CSV file. Using TensorFlow, a machine learning model
is trained on 75% of the data and its performance is evaluated on the 25%. The model is
continually tweaked using methods such as adding additional model layers, using regularization
techniques, and/or changing the dropout value until the test accuracy is sufficiently high.
Once the model is trained and optimized, it is saved so that it can be used to classify
companies as “ethical” or “unethical” without needing to be trained again. This is useful
because training a machine learning model is a time consuming process.
*TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.
The data visualization portion of Re-maximizing is the part of our code which is responsible for taking data about the number of cases of ethical misconduct, severity of misconduct, or likelihood of misconduct and displaying that to the user in an intuitive and informative manner. Re-maximizing performs data visualization using a variety of Python libraries intended for this purpose.
The above is an example of a data visualization generated using the plotly library.
The Collection of User Stories gives us feedback from consumers who have used Re-Maximizing in their line of work and demonstrates the wide array of applications that Re-Maximizing can take on. The following video contains the collection of user stories.
This is our final presentation website, a component of our final product! Our code is accessible on github at this link.


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Item 4.11.10.b. The Reock score of Massachusetts' 6th district is 0.45 while the Reock score of its 5th district is 0.23, thus the 6th district is more compact.


Before Change
def launch_command(self):
super().launch_command()
run_opts = ["--mount=type=bind,source="%s",destination="%s"" %
mp for mp in self.mount_points]
if self.with_mpi:
run_opts.append("--mpi")
run_opts += self.options
run_cmd = self._command + " run %s %s bash -c " % (" ".join(run_opts),
self.image)
return run_cmd + """ + " ".join(
["cd " + self.workdir] + self.commands) + """
class Shifter(Sarus):
"""Container platform backend for running containers with `Shifter
After Change
run_opts += self.options
workdir_opt = f"--workdir="{self.workdir}" " if self.workdir else ""
return (f"sarus run {workdir_opt}{" ".join(run_opts)} {self.image} "
f"{self.command or ""}").rstrip()
class Shifter(ContainerPlatform):
"""Container platform backend for running containers with `Shifter


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If you did not recieve and email, you can Resend the confirmation email


Proxy backend for complex types.The ComplexProxy class serves as a backend for the Proxy class. It is used in case the data type represented by the proxy is a complex number and augments the Proxy interface by the complete interface required of complex numbers. More...
#include <ComplexProxy.h>
Public Types | |
| using | value_type = typename CT::value_type |
| Value type of the represented complex element. | |
| using | ValueType = value_type |
| Value type of the represented complex element. | |
Public Member Functions | |
Data access functions | |
| ValueType | real () const |
| Returns the real part of the represented complex number. More... | |
| void | real (ValueType value) const |
| Setting the real part of the represented complex number. More... | |
| ValueType | imag () const |
| Returns the imaginary part of the represented complex number. More... | |
| void | imag (ValueType value) const |
| Setting the imaginary part of the represented complex number. More... | |
Conversion operators | |
| BLAZE_ALWAYS_INLINE PT & | operator~ () |
| Conversion operator for non-constant proxies. More... | |
| BLAZE_ALWAYS_INLINE const PT & | operator~ () const |
| Conversion operator for constant proxies. More... | |
Proxy backend for complex types.
The ComplexProxy class serves as a backend for the Proxy class. It is used in case the data type represented by the proxy is a complex number and augments the Proxy interface by the complete interface required of complex numbers.
|
inline |
Returns the imaginary part of the represented complex number.
This function returns the current value of the imaginary part of the represented complex number.
|
inline |
Setting the imaginary part of the represented complex number.
| value | The new value for the imaginary part. |
This function sets a new value to the imaginary part of the represented complex number.
| BLAZE_ALWAYS_INLINE PT & blaze::ComplexProxy< PT, CT >::operator~ | ( | ) |
Conversion operator for non-constant proxies.
This function provides a type-safe downcast to the actual type of the proxy.
| BLAZE_ALWAYS_INLINE const PT & blaze::ComplexProxy< PT, CT >::operator~ | ( | ) | const |
Conversion operator for constant proxies.
This function provides a type-safe downcast to the actual type of the proxy.
|
inline |
Returns the real part of the represented complex number.
This function returns the current value of the real part of the represented complex number.
|
inline |
Setting the real part of the represented complex number.
| value | The new value for the real part. |
This function sets a new value to the real part of the represented complex number.


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