| Name | Description |
|---|
| Cache | Persist this RDD with the default storage level . |
| Persist | Set this RDD's storage level to persist its values across operations after the first time it is computed. This can only be used to assign a new storage level if the RDD does not have a storage level set yet. If no storage level is specified defaults to . sc.Parallelize(new string[] {"b", "a", "c").Persist().isCached True |
| Unpersist | Mark the RDD as non-persistent, and remove all blocks for it from memory and disk. |
| Checkpoint | Mark this RDD for checkpointing. It will be saved to a file inside the checkpoint directory set with ) and all references to its parent RDDs will be removed. This function must be called before any job has been executed on this RDD. It is strongly recommended that this RDD is persisted in memory, otherwise saving it on a file will require recomputation. |
| GetNumPartitions | Returns the number of partitions of this RDD. |
| Map``1 | Return a new RDD by applying a function to each element of this RDD. sc.Parallelize(new string[]{"b", "a", "c"}, 1).Map(x => new KeyValuePair<string, int>(x, 1)).Collect() [('a', 1), ('b', 1), ('c', 1)] |
| FlatMap``1 | Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results. sc.Parallelize(new int[] {2, 3, 4}, 1).FlatMap(x => Enumerable.Range(1, x - 1)).Collect() [1, 1, 1, 2, 2, 3] |
| MapPartitions``1 | Return a new RDD by applying a function to each partition of this RDD. sc.Parallelize(new int[] {1, 2, 3, 4}, 2).MapPartitions(iter => new[]{iter.Sum(x => (x as decimal?))}).Collect() [3, 7] |
| MapPartitionsWithIndex``1 | Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition. sc.Parallelize(new int[]{1, 2, 3, 4}, 4).MapPartitionsWithIndex<double>((pid, iter) => (double)pid).Sum() 6 |
| Filter | Return a new RDD containing only the elements that satisfy a predicate. sc.Parallelize(new int[]{1, 2, 3, 4, 5}, 1).Filter(x => x % 2 == 0).Collect() [2, 4] |
| Distinct | Return a new RDD containing the distinct elements in this RDD. >>> sc.Parallelize(new int[] {1, 1, 2, 3}, 1).Distinct().Collect() [1, 2, 3] |
| Sample | Return a sampled subset of this RDD. var rdd = sc.Parallelize(Enumerable.Range(0, 100), 4) 6 <= rdd.Sample(False, 0.1, 81).count() <= 14 true |
| RandomSplit | Randomly splits this RDD with the provided weights. var rdd = sc.Parallelize(Enumerable.Range(0, 500), 1) var rdds = rdd.RandomSplit(new double[] {2, 3}, 17) 150 < rdds[0].Count() < 250 250 < rdds[1].Count() < 350 |
| TakeSample | Return a fixed-size sampled subset of this RDD. var rdd = sc.Parallelize(Enumerable.Range(0, 10), 2) rdd.TakeSample(true, 20, 1).Length 20 rdd.TakeSample(false, 5, 2).Length 5 rdd.TakeSample(false, 15, 3).Length 10 |
| ComputeFractionForSampleSize | Returns a sampling rate that guarantees a sample of size >= sampleSizeLowerBound 99.99% of the time. How the sampling rate is determined: Let p = num / total, where num is the sample size and total is the total number of data points in the RDD. We're trying to compute q > p such that - when sampling with replacement, we're drawing each data point with prob_i ~ Pois(q), where we want to guarantee Pr[s < num] < 0.0001 for s = sum(prob_i for i from 0 to total), i.e. the failure rate of not having a sufficiently large sample < 0.0001. Setting q = p + 5 * sqrt(p/total) is sufficient to guarantee 0.9999 success rate for num > 12, but we need a slightly larger q (9 empirically determined). - when sampling without replacement, we're drawing each data point with prob_i ~ Binomial(total, fraction) and our choice of q guarantees 1-delta, or 0.9999 success rate, where success rate is defined the same as in sampling with replacement. |
| Union | Return the union of this RDD and another one. var rdd = sc.Parallelize(new int[] { 1, 1, 2, 3 }, 1) rdd.union(rdd).collect() [1, 1, 2, 3, 1, 1, 2, 3] |
| Intersection | Return the intersection of this RDD and another one. The output will not contain any duplicate elements, even if the input RDDs did. Note that this method performs a shuffle internally. var rdd1 = sc.Parallelize(new int[] { 1, 10, 2, 3, 4, 5 }, 1) var rdd2 = sc.Parallelize(new int[] { 1, 6, 2, 3, 7, 8 }, 1) var rdd1.Intersection(rdd2).Collect() [1, 2, 3] |
| Glom | Return an RDD created by coalescing all elements within each partition into a list. var rdd = sc.Parallelize(new int[] { 1, 2, 3, 4 }, 2) rdd.Glom().Collect() [[1, 2], [3, 4]] |
| Cartesian``1 | Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of elements (a, b) where a is in self and b is in other. rdd = sc.Parallelize(new int[] { 1, 2 }, 1) rdd.Cartesian(rdd).Collect() [(1, 1), (1, 2), (2, 1), (2, 2)] |
| GroupBy``1 | Return an RDD of grouped items. Each group consists of a key and a sequence of elements mapping to that key. The ordering of elements within each group is not guaranteed, and may even differ each time the resulting RDD is evaluated. Note: This operation may be very expensive. If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]] or [[PairRDDFunctions.reduceByKey]] will provide much better performance. >>> rdd = sc.Parallelize(new int[] { 1, 1, 2, 3, 5, 8 }, 1) >>> result = rdd.GroupBy(lambda x: x % 2).Collect() [(0, [2, 8]), (1, [1, 1, 3, 5])] |
| Pipe | Return an RDD created by piping elements to a forked external process. >>> sc.Parallelize(new char[] { '1', '2', '3', '4' }, 1).Pipe("cat").Collect() [u'1', u'2', u'3', u'4'] |
| Foreach | Applies a function to all elements of this RDD. sc.Parallelize(new int[] { 1, 2, 3, 4, 5 }, 1).Foreach(x => Console.Write(x)) |
| ForeachPartition | Applies a function to each partition of this RDD. sc.parallelize(new int[] { 1, 2, 3, 4, 5 }, 1).ForeachPartition(iter => { foreach (var x in iter) Console.Write(x + " "); }) |
| Collect | Return a list that contains all of the elements in this RDD. |
| Reduce | Reduces the elements of this RDD using the specified commutative and associative binary operator. sc.Parallelize(new int[] { 1, 2, 3, 4, 5 }, 1).Reduce((x, y) => x + y) 15 |
| TreeReduce | Reduces the elements of this RDD in a multi-level tree pattern. >>> add = lambda x, y: x + y >>> rdd = sc.Parallelize(new int[] { -5, -4, -3, -2, -1, 1, 2, 3, 4 }, 10).TreeReduce((x, y) => x + y)) >>> rdd.TreeReduce(add) -5 >>> rdd.TreeReduce(add, 1) -5 >>> rdd.TreeReduce(add, 2) -5 >>> rdd.TreeReduce(add, 5) -5 >>> rdd.TreeReduce(add, 10) -5 |
| Fold | Aggregate the elements of each partition, and then the results for all the partitions, using a given associative and commutative function and a neutral "zero value." The function op(t1, t2) is allowed to modify t1 and return it as its result value to avoid object allocation; however, it should not modify t2. This behaves somewhat differently from fold operations implemented for non-distributed collections in functional languages like Scala. This fold operation may be applied to partitions individually, and then fold those results into the final result, rather than apply the fold to each element sequentially in some defined ordering. For functions that are not commutative, the result may differ from that of a fold applied to a non-distributed collection. >>> from operator import add >>> sc.parallelize([1, 2, 3, 4, 5]).fold(0, add) 15 |
| Aggregate``1 | Aggregate the elements of each partition, and then the results for all the partitions, using a given combine functions and a neutral "zero value." The functions op(t1, t2) is allowed to modify t1 and return it as its result value to avoid object allocation; however, it should not modify t2. The first function (seqOp) can return a different result type, U, than the type of this RDD. Thus, we need one operation for merging a T into an U and one operation for merging two U >>> sc.parallelize(new int[] { 1, 2, 3, 4 }, 1).Aggregate(0, (x, y) => x + y, (x, y) => x + y)) 10 |
| TreeAggregate``1 | Aggregates the elements of this RDD in a multi-level tree pattern. rdd = sc.Parallelize(new int[] { 1, 2, 3, 4 }, 1).TreeAggregate(0, (x, y) => x + y, (x, y) => x + y)) 10 |
| Count | Return the number of elements in this RDD. |
| CountByValue | Return the count of each unique value in this RDD as a dictionary of (value, count) pairs. sc.Parallelize(new int[] { 1, 2, 1, 2, 2 }, 2).CountByValue()) [(1, 2), (2, 3)] |
| Take | Take the first num elements of the RDD. It works by first scanning one partition, and use the results from that partition to estimate the number of additional partitions needed to satisfy the limit. Translated from the Scala implementation in RDD#take(). sc.Parallelize(new int[] { 2, 3, 4, 5, 6 }, 2).Cache().Take(2))) [2, 3] sc.Parallelize(new int[] { 2, 3, 4, 5, 6 }, 2).Take(10) [2, 3, 4, 5, 6] sc.Parallelize(Enumerable.Range(0, 100), 100).Filter(x => x > 90).Take(3) [91, 92, 93] |
| First | Return the first element in this RDD. >>> sc.Parallelize(new int[] { 2, 3, 4 }, 2).First() 2 |
| IsEmpty | Returns true if and only if the RDD contains no elements at all. Note that an RDD may be empty even when it has at least 1 partition. sc.Parallelize(new int[0], 1).isEmpty() true sc.Parallelize(new int[] {1}).isEmpty() false |
| Subtract | Return each value in this RDD that is not contained in . var x = sc.Parallelize(new int[] { 1, 2, 3, 4 }, 1) var y = sc.Parallelize(new int[] { 3 }, 1) x.Subtract(y).Collect()) [1, 2, 4] |
| KeyBy``1 | Creates tuples of the elements in this RDD by applying . sc.Parallelize(new int[] { 1, 2, 3, 4 }, 1).KeyBy(x => x * x).Collect()) (1, 1), (4, 2), (9, 3), (16, 4) |
| Repartition | Return a new RDD that has exactly numPartitions partitions. Can increase or decrease the level of parallelism in this RDD. Internally, this uses a shuffle to redistribute data. If you are decreasing the number of partitions in this RDD, consider using `Coalesce`, which can avoid performing a shuffle. var rdd = sc.Parallelize(new int[] { 1, 2, 3, 4, 5, 6, 7 }, 4) rdd.Glom().Collect().Length 4 rdd.Repartition(2).Glom().Collect().Length 2 |
| Coalesce | Return a new RDD that is reduced into `numPartitions` partitions. sc.Parallelize(new int[] { 1, 2, 3, 4, 5 }, 3).Glom().Collect().Length 3 >>> sc.Parallelize(new int[] { 1, 2, 3, 4, 5 }, 3).Coalesce(1).Glom().Collect().Length 1 |
| Zip``1 | Zips this RDD with another one, returning key-value pairs with the first element in each RDD second element in each RDD, etc. Assumes that the two RDDs have the same number of partitions and the same number of elements in each partition (e.g. one was made through a map on the other). var x = sc.parallelize(range(0,5)) var y = sc.parallelize(range(1000, 1005)) x.Zip(y).Collect() [(0, 1000), (1, 1001), (2, 1002), (3, 1003), (4, 1004)] |
| ZipWithIndex | Zips this RDD with its element indices. The ordering is first based on the partition index and then the ordering of items within each partition. So the first item in the first partition gets index 0, and the last item in the last partition receives the largest index. This method needs to trigger a spark job when this RDD contains more than one partitions. sc.Parallelize(new string[] { "a", "b", "c", "d" }, 3).ZipWithIndex().Collect() [('a', 0), ('b', 1), ('c', 2), ('d', 3)] |
| ZipWithUniqueId | Zips this RDD with generated unique Long ids. Items in the kth partition will get ids k, n+k, 2*n+k, ..., where n is the number of partitions. So there may exist gaps, but this method won't trigger a spark job, which is different from >>> sc.Parallelize(new string[] { "a", "b", "c", "d" }, 1).ZipWithIndex().Collect() [('a', 0), ('b', 1), ('c', 4), ('d', 2), ('e', 5)] |
| SetName | Assign a name to this RDD. >>> rdd1 = sc.parallelize([1, 2]) >>> rdd1.setName('RDD1').name() u'RDD1' |
| ToDebugString | A description of this RDD and its recursive dependencies for debugging. |
| GetStorageLevel | Get the RDD's current storage level. >>> rdd1 = sc.parallelize([1,2]) >>> rdd1.getStorageLevel() StorageLevel(False, False, False, False, 1) >>> print(rdd1.getStorageLevel()) Serialized 1x Replicated |
| ToLocalIterator | Return an iterator that contains all of the elements in this RDD. The iterator will consume as much memory as the largest partition in this RDD. sc.Parallelize(Enumerable.Range(0, 10), 1).ToLocalIterator() [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] |
| RandomSampleWithRange | Internal method exposed for Random Splits in DataFrames. Samples an RDD given a probability range. |
+| Name | Description |
|---|
| Cache | Persist this RDD with the default storage level . |
| Persist | Set this RDD's storage level to persist its values across operations after the first time it is computed. This can only be used to assign a new storage level if the RDD does not have a storage level set yet. If no storage level is specified defaults to . sc.Parallelize(new string[] {"b", "a", "c").Persist().isCached True |
| Unpersist | Mark the RDD as non-persistent, and remove all blocks for it from memory and disk. |
| Checkpoint | Mark this RDD for checkpointing. It will be saved to a file inside the checkpoint directory set with ) and all references to its parent RDDs will be removed. This function must be called before any job has been executed on this RDD. It is strongly recommended that this RDD is persisted in memory, otherwise saving it on a file will require recomputation. |
| GetNumPartitions | Returns the number of partitions of this RDD. |
| Map``1 | Return a new RDD by applying a function to each element of this RDD. sc.Parallelize(new string[]{"b", "a", "c"}, 1).Map(x => new KeyValuePair<string, int>(x, 1)).Collect() [('a', 1), ('b', 1), ('c', 1)] |
| FlatMap``1 | Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results. sc.Parallelize(new int[] {2, 3, 4}, 1).FlatMap(x => Enumerable.Range(1, x - 1)).Collect() [1, 1, 1, 2, 2, 3] |
| MapPartitions``1 | Return a new RDD by applying a function to each partition of this RDD. sc.Parallelize(new int[] {1, 2, 3, 4}, 2).MapPartitions(iter => new[]{iter.Sum(x => (x as decimal?))}).Collect() [3, 7] |
| MapPartitionsWithIndex``1 | Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition. sc.Parallelize(new int[]{1, 2, 3, 4}, 4).MapPartitionsWithIndex<double>((pid, iter) => (double)pid).Sum() 6 |
| Filter | Return a new RDD containing only the elements that satisfy a predicate. sc.Parallelize(new int[]{1, 2, 3, 4, 5}, 1).Filter(x => x % 2 == 0).Collect() [2, 4] |
| Distinct | Return a new RDD containing the distinct elements in this RDD. >>> sc.Parallelize(new int[] {1, 1, 2, 3}, 1).Distinct().Collect() [1, 2, 3] |
| Sample | Return a sampled subset of this RDD. var rdd = sc.Parallelize(Enumerable.Range(0, 100), 4) 6 <= rdd.Sample(False, 0.1, 81).count() <= 14 true |
| RandomSplit | Randomly splits this RDD with the provided weights. var rdd = sc.Parallelize(Enumerable.Range(0, 500), 1) var rdds = rdd.RandomSplit(new double[] {2, 3}, 17) 150 < rdds[0].Count() < 250 250 < rdds[1].Count() < 350 |
| TakeSample | Return a fixed-size sampled subset of this RDD. var rdd = sc.Parallelize(Enumerable.Range(0, 10), 2) rdd.TakeSample(true, 20, 1).Length 20 rdd.TakeSample(false, 5, 2).Length 5 rdd.TakeSample(false, 15, 3).Length 10 |
| ComputeFractionForSampleSize | Returns a sampling rate that guarantees a sample of size >= sampleSizeLowerBound 99.99% of the time. How the sampling rate is determined: Let p = num / total, where num is the sample size and total is the total number of data points in the RDD. We're trying to compute q > p such that - when sampling with replacement, we're drawing each data point with prob_i ~ Pois(q), where we want to guarantee Pr[s < num] < 0.0001 for s = sum(prob_i for i from 0 to total), i.e. the failure rate of not having a sufficiently large sample < 0.0001. Setting q = p + 5 * sqrt(p/total) is sufficient to guarantee 0.9999 success rate for num > 12, but we need a slightly larger q (9 empirically determined). - when sampling without replacement, we're drawing each data point with prob_i ~ Binomial(total, fraction) and our choice of q guarantees 1-delta, or 0.9999 success rate, where success rate is defined the same as in sampling with replacement. |
| Union | Return the union of this RDD and another one. var rdd = sc.Parallelize(new int[] { 1, 1, 2, 3 }, 1) rdd.union(rdd).collect() [1, 1, 2, 3, 1, 1, 2, 3] |
| Intersection | Return the intersection of this RDD and another one. The output will not contain any duplicate elements, even if the input RDDs did. Note that this method performs a shuffle internally. var rdd1 = sc.Parallelize(new int[] { 1, 10, 2, 3, 4, 5 }, 1) var rdd2 = sc.Parallelize(new int[] { 1, 6, 2, 3, 7, 8 }, 1) var rdd1.Intersection(rdd2).Collect() [1, 2, 3] |
| Glom | Return an RDD created by coalescing all elements within each partition into a list. var rdd = sc.Parallelize(new int[] { 1, 2, 3, 4 }, 2) rdd.Glom().Collect() [[1, 2], [3, 4]] |
| Cartesian``1 | Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of elements (a, b) where a is in self and b is in other. rdd = sc.Parallelize(new int[] { 1, 2 }, 1) rdd.Cartesian(rdd).Collect() [(1, 1), (1, 2), (2, 1), (2, 2)] |
| GroupBy``1 | Return an RDD of grouped items. Each group consists of a key and a sequence of elements mapping to that key. The ordering of elements within each group is not guaranteed, and may even differ each time the resulting RDD is evaluated. Note: This operation may be very expensive. If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]] or [[PairRDDFunctions.reduceByKey]] will provide much better performance. >>> rdd = sc.Parallelize(new int[] { 1, 1, 2, 3, 5, 8 }, 1) >>> result = rdd.GroupBy(lambda x: x % 2).Collect() [(0, [2, 8]), (1, [1, 1, 3, 5])] |
| Pipe | Return an RDD created by piping elements to a forked external process. >>> sc.Parallelize(new char[] { '1', '2', '3', '4' }, 1).Pipe("cat").Collect() [u'1', u'2', u'3', u'4'] |
| Foreach | Applies a function to all elements of this RDD. sc.Parallelize(new int[] { 1, 2, 3, 4, 5 }, 1).Foreach(x => Console.Write(x)) |
| ForeachPartition | Applies a function to each partition of this RDD. sc.parallelize(new int[] { 1, 2, 3, 4, 5 }, 1).ForeachPartition(iter => { foreach (var x in iter) Console.Write(x + " "); }) |
| Collect | Return a list that contains all of the elements in this RDD. |
| Reduce | Reduces the elements of this RDD using the specified commutative and associative binary operator. sc.Parallelize(new int[] { 1, 2, 3, 4, 5 }, 1).Reduce((x, y) => x + y) 15 |
| TreeReduce | Reduces the elements of this RDD in a multi-level tree pattern. >>> add = lambda x, y: x + y >>> rdd = sc.Parallelize(new int[] { -5, -4, -3, -2, -1, 1, 2, 3, 4 }, 10).TreeReduce((x, y) => x + y)) >>> rdd.TreeReduce(add) -5 >>> rdd.TreeReduce(add, 1) -5 >>> rdd.TreeReduce(add, 2) -5 >>> rdd.TreeReduce(add, 5) -5 >>> rdd.TreeReduce(add, 10) -5 |
| Fold | Aggregate the elements of each partition, and then the results for all the partitions, using a given associative and commutative function and a neutral "zero value." The function op(t1, t2) is allowed to modify t1 and return it as its result value to avoid object allocation; however, it should not modify t2. This behaves somewhat differently from fold operations implemented for non-distributed collections in functional languages like Scala. This fold operation may be applied to partitions individually, and then fold those results into the final result, rather than apply the fold to each element sequentially in some defined ordering. For functions that are not commutative, the result may differ from that of a fold applied to a non-distributed collection. >>> from operator import add >>> sc.parallelize([1, 2, 3, 4, 5]).fold(0, add) 15 |
| Aggregate``1 | Aggregate the elements of each partition, and then the results for all the partitions, using a given combine functions and a neutral "zero value." The functions op(t1, t2) is allowed to modify t1 and return it as its result value to avoid object allocation; however, it should not modify t2. The first function (seqOp) can return a different result type, U, than the type of this RDD. Thus, we need one operation for merging a T into an U and one operation for merging two U >>> sc.parallelize(new int[] { 1, 2, 3, 4 }, 1).Aggregate(0, (x, y) => x + y, (x, y) => x + y)) 10 |
| TreeAggregate``1 | Aggregates the elements of this RDD in a multi-level tree pattern. rdd = sc.Parallelize(new int[] { 1, 2, 3, 4 }, 1).TreeAggregate(0, (x, y) => x + y, (x, y) => x + y)) 10 |
| Count | Return the number of elements in this RDD. |
| CountByValue | Return the count of each unique value in this RDD as a dictionary of (value, count) pairs. sc.Parallelize(new int[] { 1, 2, 1, 2, 2 }, 2).CountByValue()) [(1, 2), (2, 3)] |
| Take | Take the first num elements of the RDD. It works by first scanning one partition, and use the results from that partition to estimate the number of additional partitions needed to satisfy the limit. Translated from the Scala implementation in RDD#take(). sc.Parallelize(new int[] { 2, 3, 4, 5, 6 }, 2).Cache().Take(2))) [2, 3] sc.Parallelize(new int[] { 2, 3, 4, 5, 6 }, 2).Take(10) [2, 3, 4, 5, 6] sc.Parallelize(Enumerable.Range(0, 100), 100).Filter(x => x > 90).Take(3) [91, 92, 93] |
| First | Return the first element in this RDD. >>> sc.Parallelize(new int[] { 2, 3, 4 }, 2).First() 2 |
| IsEmpty | Returns true if and only if the RDD contains no elements at all. Note that an RDD may be empty even when it has at least 1 partition. sc.Parallelize(new int[0], 1).isEmpty() true sc.Parallelize(new int[] {1}).isEmpty() false |
| Subtract | Return each value in this RDD that is not contained in . var x = sc.Parallelize(new int[] { 1, 2, 3, 4 }, 1) var y = sc.Parallelize(new int[] { 3 }, 1) x.Subtract(y).Collect()) [1, 2, 4] |
| KeyBy``1 | Creates tuples of the elements in this RDD by applying . sc.Parallelize(new int[] { 1, 2, 3, 4 }, 1).KeyBy(x => x * x).Collect()) (1, 1), (4, 2), (9, 3), (16, 4) |
| Repartition | Return a new RDD that has exactly numPartitions partitions. Can increase or decrease the level of parallelism in this RDD. Internally, this uses a shuffle to redistribute data. If you are decreasing the number of partitions in this RDD, consider using `Coalesce`, which can avoid performing a shuffle. var rdd = sc.Parallelize(new int[] { 1, 2, 3, 4, 5, 6, 7 }, 4) rdd.Glom().Collect().Length 4 rdd.Repartition(2).Glom().Collect().Length 2 |
| Coalesce | Return a new RDD that is reduced into `numPartitions` partitions. sc.Parallelize(new int[] { 1, 2, 3, 4, 5 }, 3).Glom().Collect().Length 3 >>> sc.Parallelize(new int[] { 1, 2, 3, 4, 5 }, 3).Coalesce(1).Glom().Collect().Length 1 |
| Zip``1 | Zips this RDD with another one, returning key-value pairs with the first element in each RDD second element in each RDD, etc. Assumes that the two RDDs have the same number of partitions and the same number of elements in each partition (e.g. one was made through a map on the other). var x = sc.parallelize(range(0,5)) var y = sc.parallelize(range(1000, 1005)) x.Zip(y).Collect() [(0, 1000), (1, 1001), (2, 1002), (3, 1003), (4, 1004)] |
| ZipWithIndex | Zips this RDD with its element indices. The ordering is first based on the partition index and then the ordering of items within each partition. So the first item in the first partition gets index 0, and the last item in the last partition receives the largest index. This method needs to trigger a spark job when this RDD contains more than one partitions. sc.Parallelize(new string[] { "a", "b", "c", "d" }, 3).ZipWithIndex().Collect() [('a', 0), ('b', 1), ('c', 2), ('d', 3)] |
| ZipWithUniqueId | Zips this RDD with generated unique Long ids. Items in the kth partition will get ids k, n+k, 2*n+k, ..., where n is the number of partitions. So there may exist gaps, but this method won't trigger a spark job, which is different from >>> sc.Parallelize(new string[] { "a", "b", "c", "d" }, 1).ZipWithIndex().Collect() [('a', 0), ('b', 1), ('c', 4), ('d', 2), ('e', 5)] |
| SetName | Assign a name to this RDD. >>> rdd1 = sc.parallelize([1, 2]) >>> rdd1.setName('RDD1').name() u'RDD1' |
| ToDebugString | A description of this RDD and its recursive dependencies for debugging. |
| GetStorageLevel | Get the RDD's current storage level. >>> rdd1 = sc.parallelize([1,2]) >>> rdd1.getStorageLevel() StorageLevel(False, False, False, False, 1) >>> print(rdd1.getStorageLevel()) Serialized 1x Replicated |
| ToLocalIterator | Return an iterator that contains all of the elements in this RDD. The iterator will consume as much memory as the largest partition in this RDD. sc.Parallelize(Enumerable.Range(0, 10), 1).ToLocalIterator() [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] |
| RandomSampleWithRange | Internal method exposed for Random Splits in DataFrames. Samples an RDD given a probability range. |
+
+---
+
+
###| Name | Description |
|---|
| GetActiveSparkContext | Get existing SparkContext |
| TextFile | Read a text file from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI, and return it as an RDD of Strings. |
| Parallelize``1 | Distribute a local collection to form an RDD. sc.Parallelize(new int[] {0, 2, 3, 4, 6}, 5).Glom().Collect() [[0], [2], [3], [4], [6]] |
| EmptyRDD | Create an RDD that has no partitions or elements. |
| WholeTextFiles | Read a directory of text files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI. Each file is read as a single record and returned in a key-value pair, where the key is the path of each file, the value is the content of each file. For example, if you have the following files: {{{ hdfs://a-hdfs-path/part-00000 hdfs://a-hdfs-path/part-00001 ... hdfs://a-hdfs-path/part-nnnnn }}} Do {{{ RDD<KeyValuePair<string, string>> rdd = sparkContext.WholeTextFiles("hdfs://a-hdfs-path") }}} then `rdd` contains {{{ (a-hdfs-path/part-00000, its content) (a-hdfs-path/part-00001, its content) ... (a-hdfs-path/part-nnnnn, its content) }}} Small files are preferred, large file is also allowable, but may cause bad performance. minPartitions A suggestion value of the minimal splitting number for input data. |
| BinaryFiles | Read a directory of binary files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI as a byte array. Each file is read as a single record and returned in a key-value pair, where the key is the path of each file, the value is the content of each file. For example, if you have the following files: {{{ hdfs://a-hdfs-path/part-00000 hdfs://a-hdfs-path/part-00001 ... hdfs://a-hdfs-path/part-nnnnn }}} Do RDD<KeyValuePair<string, byte[]>>"/> rdd = sparkContext.dataStreamFiles("hdfs://a-hdfs-path")`, then `rdd` contains {{{ (a-hdfs-path/part-00000, its content) (a-hdfs-path/part-00001, its content) ... (a-hdfs-path/part-nnnnn, its content) }}} @note Small files are preferred; very large files but may cause bad performance. @param minPartitions A suggestion value of the minimal splitting number for input data. |
| SequenceFile | Read a Hadoop SequenceFile with arbitrary key and value Writable class from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI. The mechanism is as follows: 1. A Java RDD is created from the SequenceFile or other InputFormat, and the key and value Writable classes 2. Serialization is attempted via Pyrolite pickling 3. If this fails, the fallback is to call 'toString' on each key and value 4. PickleSerializer is used to deserialize pickled objects on the Python side |
| NewAPIHadoopFile | Read a 'new API' Hadoop InputFormat with arbitrary key and value class from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI. The mechanism is the same as for sc.sequenceFile. A Hadoop configuration can be passed in as a Python dict. This will be converted into a Configuration in Java |
| NewAPIHadoopRDD | Read a 'new API' Hadoop InputFormat with arbitrary key and value class, from an arbitrary Hadoop configuration, which is passed in as a Python dict. This will be converted into a Configuration in Java. The mechanism is the same as for sc.sequenceFile. |
| HadoopFile | Read an 'old' Hadoop InputFormat with arbitrary key and value class from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI. The mechanism is the same as for sc.sequenceFile. A Hadoop configuration can be passed in as a Python dict. This will be converted into a Configuration in Java. |
| HadoopRDD | Read an 'old' Hadoop InputFormat with arbitrary key and value class, from an arbitrary Hadoop configuration, which is passed in as a Python dict. This will be converted into a Configuration in Java. The mechanism is the same as for sc.sequenceFile. |
| Union``1 | Build the union of a list of RDDs. This supports unions() of RDDs with different serialized formats, although this forces them to be reserialized using the default serializer: >>> path = os.path.join(tempdir, "union-text.txt") >>> with open(path, "w") as testFile: ... _ = testFile.write("Hello") >>> textFile = sc.textFile(path) >>> textFile.collect() [u'Hello'] >>> parallelized = sc.parallelize(["World!"]) >>> sorted(sc.union([textFile, parallelized]).collect()) [u'Hello', 'World!'] |
| Broadcast``1 | Broadcast a read-only variable to the cluster, returning a Broadcast object for reading it in distributed functions. The variable will be sent to each cluster only once. |
| Accumulator``1 | Create an with the given initial value, using a given helper object to define how to add values of the data type if provided. Default AccumulatorParams are used for integers and floating-point numbers if you do not provide one. For other types, a custom AccumulatorParam can be used. |
| Stop | Shut down the SparkContext. |
| AddFile | Add a file to be downloaded with this Spark job on every node. The `path` passed can be either a local file, a file in HDFS (or other Hadoop-supported filesystems), or an HTTP, HTTPS or FTP URI. To access the file in Spark jobs, use `SparkFiles.get(fileName)` to find its download location. |
| SetCheckpointDir | Set the directory under which RDDs are going to be checkpointed. The directory must be a HDFS path if running on a cluster. |
| SetJobGroup | Assigns a group ID to all the jobs started by this thread until the group ID is set to a different value or cleared. Often, a unit of execution in an application consists of multiple Spark actions or jobs. Application programmers can use this method to group all those jobs together and give a group description. Once set, the Spark web UI will associate such jobs with this group. The application can also use [[org.apache.spark.api.java.JavaSparkContext.cancelJobGroup]] to cancel all running jobs in this group. For example, {{{ // In the main thread: sc.setJobGroup("some_job_to_cancel", "some job description"); rdd.map(...).count(); // In a separate thread: sc.cancelJobGroup("some_job_to_cancel"); }}} If interruptOnCancel is set to true for the job group, then job cancellation will result in Thread.interrupt() being called on the job's executor threads. This is useful to help ensure that the tasks are actually stopped in a timely manner, but is off by default due to HDFS-1208, where HDFS may respond to Thread.interrupt() by marking nodes as dead. |
| SetLocalProperty | Set a local property that affects jobs submitted from this thread, such as the Spark fair scheduler pool. |
| GetLocalProperty | Get a local property set in this thread, or null if it is missing. See [[org.apache.spark.api.java.JavaSparkContext.setLocalProperty]]. |
| SetLogLevel | Control our logLevel. This overrides any user-defined log settings. @param logLevel The desired log level as a string. Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, WARN |
| CancelJobGroup | Cancel active jobs for the specified group. See for more information. |
| CancelAllJobs | Cancel all jobs that have been scheduled or are running. |
+| Name | Description |
|---|
| GetActiveSparkContext | Get existing SparkContext |
| GetConf | Return a copy of this JavaSparkContext's configuration. The configuration ''cannot'' be changed at runtime. |
| TextFile | Read a text file from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI, and return it as an RDD of Strings. |
| Parallelize``1 | Distribute a local collection to form an RDD. sc.Parallelize(new int[] {0, 2, 3, 4, 6}, 5).Glom().Collect() [[0], [2], [3], [4], [6]] |
| EmptyRDD | Create an RDD that has no partitions or elements. |
| WholeTextFiles | Read a directory of text files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI. Each file is read as a single record and returned in a key-value pair, where the key is the path of each file, the value is the content of each file. For example, if you have the following files: {{{ hdfs://a-hdfs-path/part-00000 hdfs://a-hdfs-path/part-00001 ... hdfs://a-hdfs-path/part-nnnnn }}} Do {{{ RDD<KeyValuePair<string, string>> rdd = sparkContext.WholeTextFiles("hdfs://a-hdfs-path") }}} then `rdd` contains {{{ (a-hdfs-path/part-00000, its content) (a-hdfs-path/part-00001, its content) ... (a-hdfs-path/part-nnnnn, its content) }}} Small files are preferred, large file is also allowable, but may cause bad performance. minPartitions A suggestion value of the minimal splitting number for input data. |
| BinaryFiles | Read a directory of binary files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI as a byte array. Each file is read as a single record and returned in a key-value pair, where the key is the path of each file, the value is the content of each file. For example, if you have the following files: {{{ hdfs://a-hdfs-path/part-00000 hdfs://a-hdfs-path/part-00001 ... hdfs://a-hdfs-path/part-nnnnn }}} Do RDD<KeyValuePair<string, byte[]>>"/> rdd = sparkContext.dataStreamFiles("hdfs://a-hdfs-path")`, then `rdd` contains {{{ (a-hdfs-path/part-00000, its content) (a-hdfs-path/part-00001, its content) ... (a-hdfs-path/part-nnnnn, its content) }}} @note Small files are preferred; very large files but may cause bad performance. @param minPartitions A suggestion value of the minimal splitting number for input data. |
| SequenceFile | Read a Hadoop SequenceFile with arbitrary key and value Writable class from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI. The mechanism is as follows: 1. A Java RDD is created from the SequenceFile or other InputFormat, and the key and value Writable classes 2. Serialization is attempted via Pyrolite pickling 3. If this fails, the fallback is to call 'toString' on each key and value 4. PickleSerializer is used to deserialize pickled objects on the Python side |
| NewAPIHadoopFile | Read a 'new API' Hadoop InputFormat with arbitrary key and value class from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI. The mechanism is the same as for sc.sequenceFile. A Hadoop configuration can be passed in as a Python dict. This will be converted into a Configuration in Java |
| NewAPIHadoopRDD | Read a 'new API' Hadoop InputFormat with arbitrary key and value class, from an arbitrary Hadoop configuration, which is passed in as a Python dict. This will be converted into a Configuration in Java. The mechanism is the same as for sc.sequenceFile. |
| HadoopFile | Read an 'old' Hadoop InputFormat with arbitrary key and value class from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI. The mechanism is the same as for sc.sequenceFile. A Hadoop configuration can be passed in as a Python dict. This will be converted into a Configuration in Java. |
| HadoopRDD | Read an 'old' Hadoop InputFormat with arbitrary key and value class, from an arbitrary Hadoop configuration, which is passed in as a Python dict. This will be converted into a Configuration in Java. The mechanism is the same as for sc.sequenceFile. |
| Union``1 | Build the union of a list of RDDs. This supports unions() of RDDs with different serialized formats, although this forces them to be reserialized using the default serializer: >>> path = os.path.join(tempdir, "union-text.txt") >>> with open(path, "w") as testFile: ... _ = testFile.write("Hello") >>> textFile = sc.textFile(path) >>> textFile.collect() [u'Hello'] >>> parallelized = sc.parallelize(["World!"]) >>> sorted(sc.union([textFile, parallelized]).collect()) [u'Hello', 'World!'] |
| Broadcast``1 | Broadcast a read-only variable to the cluster, returning a Broadcast object for reading it in distributed functions. The variable will be sent to each cluster only once. |
| Accumulator``1 | Create an with the given initial value, using a given helper object to define how to add values of the data type if provided. Default AccumulatorParams are used for integers and floating-point numbers if you do not provide one. For other types, a custom AccumulatorParam can be used. |
| Stop | Shut down the SparkContext. |
| AddFile | Add a file to be downloaded with this Spark job on every node. The `path` passed can be either a local file, a file in HDFS (or other Hadoop-supported filesystems), or an HTTP, HTTPS or FTP URI. To access the file in Spark jobs, use `SparkFiles.get(fileName)` to find its download location. |
| SetCheckpointDir | Set the directory under which RDDs are going to be checkpointed. The directory must be a HDFS path if running on a cluster. |
| SetJobGroup | Assigns a group ID to all the jobs started by this thread until the group ID is set to a different value or cleared. Often, a unit of execution in an application consists of multiple Spark actions or jobs. Application programmers can use this method to group all those jobs together and give a group description. Once set, the Spark web UI will associate such jobs with this group. The application can also use [[org.apache.spark.api.java.JavaSparkContext.cancelJobGroup]] to cancel all running jobs in this group. For example, {{{ // In the main thread: sc.setJobGroup("some_job_to_cancel", "some job description"); rdd.map(...).count(); // In a separate thread: sc.cancelJobGroup("some_job_to_cancel"); }}} If interruptOnCancel is set to true for the job group, then job cancellation will result in Thread.interrupt() being called on the job's executor threads. This is useful to help ensure that the tasks are actually stopped in a timely manner, but is off by default due to HDFS-1208, where HDFS may respond to Thread.interrupt() by marking nodes as dead. |
| SetLocalProperty | Set a local property that affects jobs submitted from this thread, such as the Spark fair scheduler pool. |
| GetLocalProperty | Get a local property set in this thread, or null if it is missing. See [[org.apache.spark.api.java.JavaSparkContext.setLocalProperty]]. |
| SetLogLevel | Control our logLevel. This overrides any user-defined log settings. @param logLevel The desired log level as a string. Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, WARN |
| CancelJobGroup | Cancel active jobs for the specified group. See for more information. |
| CancelAllJobs | Cancel all jobs that have been scheduled or are running. |
---
@@ -629,21 +649,6 @@
---
-###| Name | Description |
|---|
| GetOrCreate | Get the existing SQLContext or create a new one with given SparkContext. |
| NewSession | Returns a new SQLContext as new session, that has separate SQLConf, registered temporary tables and UDFs, but shared SparkContext and table cache. |
| GetConf | Returns the value of Spark SQL configuration property for the given key. If the key is not set, returns defaultValue. |
| SetConf | Sets the given Spark SQL configuration property. |
| Read | Returns a DataFrameReader that can be used to read data in as a DataFrame. |
| ReadDataFrame | Loads a dataframe the source path using the given schema and options |
| CreateDataFrame | Creates a from a RDD containing array of object using the given schema. |
| RegisterDataFrameAsTable | Registers the given as a temporary table in the catalog. Temporary tables exist only during the lifetime of this instance of SqlContext. |
| DropTempTable | Remove the temp table from catalog. |
| Table | Returns the specified table as a |
| Tables | Returns a containing names of tables in the given database. If is not specified, the current database will be used. The returned DataFrame has two columns: 'tableName' and 'isTemporary' (a column with bool type indicating if a table is a temporary one or not). |
| TableNames | Returns a list of names of tables in the database |
| CacheTable | Caches the specified table in-memory. |
| UncacheTable | Removes the specified table from the in-memory cache. |
| ClearCache | Removes all cached tables from the in-memory cache. |
| IsCached | Returns true if the table is currently cached in-memory. |
| Sql | Executes a SQL query using Spark, returning the result as a DataFrame. The dialect that is used for SQL parsing can be configured with 'spark.sql.dialect' |
| JsonFile | Loads a JSON file (one object per line), returning the result as a DataFrame It goes through the entire dataset once to determine the schema. |
| JsonFile | Loads a JSON file (one object per line) and applies the given schema |
| TextFile | Loads text file with the specific column delimited using the given schema |
| TextFile | Loads a text file (one object per line), returning the result as a DataFrame |
| RegisterFunction``1 | Register UDF with no input argument, e.g: SqlContext.RegisterFunction<bool>("MyFilter", () => true); sqlContext.Sql("SELECT * FROM MyTable where MyFilter()"); |
| RegisterFunction``2 | Register UDF with 1 input argument, e.g: SqlContext.RegisterFunction<bool, string>("MyFilter", (arg1) => arg1 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1)"); |
| RegisterFunction``3 | Register UDF with 2 input arguments, e.g: SqlContext.RegisterFunction<bool, string, string>("MyFilter", (arg1, arg2) => arg1 != null && arg2 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1, columnName2)"); |
| RegisterFunction``4 | Register UDF with 3 input arguments, e.g: SqlContext.RegisterFunction<bool, string, string, string>("MyFilter", (arg1, arg2, arg3) => arg1 != null && arg2 != null && arg3 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1, columnName2, columnName3)"); |
| RegisterFunction``5 | Register UDF with 4 input arguments, e.g: SqlContext.RegisterFunction<bool, string, string, ..., string>("MyFilter", (arg1, arg2, ..., arg4) => arg1 != null && arg2 != null && ... && arg3 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1, columnName2, ..., columnName4)"); |
| RegisterFunction``6 | Register UDF with 5 input arguments, e.g: SqlContext.RegisterFunction<bool, string, string, ..., string>("MyFilter", (arg1, arg2, ..., arg5) => arg1 != null && arg2 != null && ... && arg5 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1, columnName2, ..., columnName5)"); |
| RegisterFunction``7 | Register UDF with 6 input arguments, e.g: SqlContext.RegisterFunction<bool, string, string, ..., string>("MyFilter", (arg1, arg2, ..., arg6) => arg1 != null && arg2 != null && ... && arg6 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1, columnName2, ..., columnName6)"); |
| RegisterFunction``8 | Register UDF with 7 input arguments, e.g: SqlContext.RegisterFunction<bool, string, string, ..., string>("MyFilter", (arg1, arg2, ..., arg7) => arg1 != null && arg2 != null && ... && arg7 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1, columnName2, ..., columnName7)"); |
| RegisterFunction``9 | Register UDF with 8 input arguments, e.g: SqlContext.RegisterFunction<bool, string, string, ..., string>("MyFilter", (arg1, arg2, ..., arg8) => arg1 != null && arg2 != null && ... && arg8 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1, columnName2, ..., columnName8)"); |
| RegisterFunction``10 | Register UDF with 9 input arguments, e.g: SqlContext.RegisterFunction<bool, string, string, ..., string>("MyFilter", (arg1, arg2, ..., arg9) => arg1 != null && arg2 != null && ... && arg9 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1, columnName2, ..., columnName9)"); |
| RegisterFunction``11 | Register UDF with 10 input arguments, e.g: SqlContext.RegisterFunction<bool, string, string, ..., string>("MyFilter", (arg1, arg2, ..., arg10) => arg1 != null && arg2 != null && ... && arg10 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1, columnName2, ..., columnName10)"); |
-
----
-
-
###| Name | Description |
|---|
| GetOrCreate | Get the existing SQLContext or create a new one with given SparkContext. |
| NewSession | Returns a new SQLContext as new session, that has separate SQLConf, registered temporary tables and UDFs, but shared SparkContext and table cache. |
| GetConf | Returns the value of Spark SQL configuration property for the given key. If the key is not set, returns defaultValue. |
| SetConf | Sets the given Spark SQL configuration property. |
| Read | Returns a DataFrameReader that can be used to read data in as a DataFrame. |
| ReadDataFrame | Loads a dataframe the source path using the given schema and options |
| CreateDataFrame | Creates a from a RDD containing array of object using the given schema. |
| RegisterDataFrameAsTable | Registers the given as a temporary table in the catalog. Temporary tables exist only during the lifetime of this instance of SqlContext. |
| DropTempTable | Remove the temp table from catalog. |
| Table | Returns the specified table as a |
| Tables | Returns a containing names of tables in the given database. If is not specified, the current database will be used. The returned DataFrame has two columns: 'tableName' and 'isTemporary' (a column with bool type indicating if a table is a temporary one or not). |
| TableNames | Returns a list of names of tables in the database |
| CacheTable | Caches the specified table in-memory. |
| UncacheTable | Removes the specified table from the in-memory cache. |
| ClearCache | Removes all cached tables from the in-memory cache. |
| IsCached | Returns true if the table is currently cached in-memory. |
| Sql | Executes a SQL query using Spark, returning the result as a DataFrame. The dialect that is used for SQL parsing can be configured with 'spark.sql.dialect' |
| JsonFile | Loads a JSON file (one object per line), returning the result as a DataFrame It goes through the entire dataset once to determine the schema. |
| JsonFile | Loads a JSON file (one object per line) and applies the given schema |
| TextFile | Loads text file with the specific column delimited using the given schema |
| TextFile | Loads a text file (one object per line), returning the result as a DataFrame |
| RegisterFunction``1 | Register UDF with no input argument, e.g: SqlContext.RegisterFunction<bool>("MyFilter", () => true); sqlContext.Sql("SELECT * FROM MyTable where MyFilter()"); |
| RegisterFunction``2 | Register UDF with 1 input argument, e.g: SqlContext.RegisterFunction<bool, string>("MyFilter", (arg1) => arg1 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1)"); |
| RegisterFunction``3 | Register UDF with 2 input arguments, e.g: SqlContext.RegisterFunction<bool, string, string>("MyFilter", (arg1, arg2) => arg1 != null && arg2 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1, columnName2)"); |
| RegisterFunction``4 | Register UDF with 3 input arguments, e.g: SqlContext.RegisterFunction<bool, string, string, string>("MyFilter", (arg1, arg2, arg3) => arg1 != null && arg2 != null && arg3 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1, columnName2, columnName3)"); |
| RegisterFunction``5 | Register UDF with 4 input arguments, e.g: SqlContext.RegisterFunction<bool, string, string, ..., string>("MyFilter", (arg1, arg2, ..., arg4) => arg1 != null && arg2 != null && ... && arg3 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1, columnName2, ..., columnName4)"); |
| RegisterFunction``6 | Register UDF with 5 input arguments, e.g: SqlContext.RegisterFunction<bool, string, string, ..., string>("MyFilter", (arg1, arg2, ..., arg5) => arg1 != null && arg2 != null && ... && arg5 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1, columnName2, ..., columnName5)"); |
| RegisterFunction``7 | Register UDF with 6 input arguments, e.g: SqlContext.RegisterFunction<bool, string, string, ..., string>("MyFilter", (arg1, arg2, ..., arg6) => arg1 != null && arg2 != null && ... && arg6 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1, columnName2, ..., columnName6)"); |
| RegisterFunction``8 | Register UDF with 7 input arguments, e.g: SqlContext.RegisterFunction<bool, string, string, ..., string>("MyFilter", (arg1, arg2, ..., arg7) => arg1 != null && arg2 != null && ... && arg7 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1, columnName2, ..., columnName7)"); |
| RegisterFunction``9 | Register UDF with 8 input arguments, e.g: SqlContext.RegisterFunction<bool, string, string, ..., string>("MyFilter", (arg1, arg2, ..., arg8) => arg1 != null && arg2 != null && ... && arg8 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1, columnName2, ..., columnName8)"); |
| RegisterFunction``10 | Register UDF with 9 input arguments, e.g: SqlContext.RegisterFunction<bool, string, string, ..., string>("MyFilter", (arg1, arg2, ..., arg9) => arg1 != null && arg2 != null && ... && arg9 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1, columnName2, ..., columnName9)"); |
| RegisterFunction``11 | Register UDF with 10 input arguments, e.g: SqlContext.RegisterFunction<bool, string, string, ..., string>("MyFilter", (arg1, arg2, ..., arg10) => arg1 != null && arg2 != null && ... && arg10 != null); sqlContext.Sql("SELECT * FROM MyTable where MyFilter(columnName1, columnName2, ..., columnName10)"); |
+
+---
+
+
###