Return the union of this RDD and another one.
Return the union of this RDD and another one. Any identical elements will appear multiple
times (use .distinct() to eliminate them).
Performs an aggregation over all Rows in this RDD.
Performs an aggregation over all Rows in this RDD. This is equivalent to a groupBy with no grouping expressions.
schemaRDD.aggregate(Sum('sales) as 'totalSales)Aggregate the elements of each partition, and then the results for all the partitions, using given combine functions and a neutral "zero value".
Aggregate the elements of each partition, and then the results for all the partitions, using given combine functions and a neutral "zero value". This function can return a different result type, U, than the type of this RDD, T. Thus, we need one operation for merging a T into an U and one operation for merging two U's, as in scala.TraversableOnce. Both of these functions are allowed to modify and return their first argument instead of creating a new U to avoid memory allocation.
Applies a qualifier to the attributes of this relation.
Applies a qualifier to the attributes of this relation. Can be used to disambiguate attributes with the same name, for example, when performing self-joins.
val x = schemaRDD.where('a === 1).as('x) val y = schemaRDD.where('a === 2).as('y) x.join(y).where("x.a".attr === "y.a".attr),
Overridden cache function will always use the in-memory columnar caching.
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 this and b is in other.
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 this and b is in other.
Mark this RDD for checkpointing.
Mark this RDD for checkpointing. It will be saved to a file inside the checkpoint directory set with SparkContext.setCheckpointDir() 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.
Clears the dependencies of this RDD.
Clears the dependencies of this RDD. This method must ensure that all references to the original parent RDDs is removed to enable the parent RDDs to be garbage collected. Subclasses of RDD may override this method for implementing their own cleaning logic. See org.apache.spark.rdd.UnionRDD for an example.
Return a new RDD that is reduced into numPartitions partitions.
Return a new RDD that is reduced into numPartitions partitions.
This results in a narrow dependency, e.g. if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of the 100 new partitions will claim 10 of the current partitions.
However, if you're doing a drastic coalesce, e.g. to numPartitions = 1, this may result in your computation taking place on fewer nodes than you like (e.g. one node in the case of numPartitions = 1). To avoid this, you can pass shuffle = true. This will add a shuffle step, but means the current upstream partitions will be executed in parallel (per whatever the current partitioning is).
Note: With shuffle = true, you can actually coalesce to a larger number of partitions. This is useful if you have a small number of partitions, say 100, potentially with a few partitions being abnormally large. Calling coalesce(1000, shuffle = true) will result in 1000 partitions with the data distributed using a hash partitioner.
Return an array that contains all of the elements in this RDD.
Return an RDD that contains all matching values by applying f.
Return an RDD that contains all matching values by applying f.
:: DeveloperApi :: Implemented by subclasses to compute a given partition.
The org.apache.spark.SparkContext that this RDD was created on.
The org.apache.spark.SparkContext that this RDD was created on.
:: Experimental :: Return the number of elements in the RDD.
:: Experimental :: Return the number of elements in the RDD. Unlike the base RDD implementation of count, this implementation leverages the query optimizer to compute the count on the SchemaRDD, which supports features such as filter pushdown.
:: Experimental :: Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
:: Experimental :: Approximate version of count() that returns a potentially incomplete result within a timeout, even if not all tasks have finished.
Return approximate number of distinct elements in the RDD.
Return approximate number of distinct elements in the RDD.
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.
Relative accuracy. Smaller values create counters that require more space. It must be greater than 0.000017.
:: Experimental :: Return approximate number of distinct elements in the RDD.
:: Experimental :: Return approximate number of distinct elements in the RDD.
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 > p
would trigger sparse representation of registers, which may reduce the memory consumption
and increase accuracy when the cardinality is small.
The precision value for the normal set.
p must be a value between 4 and sp if sp is not zero (32 max).
The precision value for the sparse set, between 0 and 32.
If sp equals 0, the sparse representation is skipped.
Return the count of each unique value in this RDD as a local map of (value, count) pairs.
Return the count of each unique value in this RDD as a local map of (value, count) pairs.
Note that this method should only be used if the resulting map is expected to be small, as the whole thing is loaded into the driver's memory. To handle very large results, consider using rdd.map(x => (x, 1L)).reduceByKey(_ + _), which returns an RDD[T, Long] instead of a map.
:: Experimental :: Approximate version of countByValue().
:: Experimental :: Approximate version of countByValue().
Get the list of dependencies of this RDD, taking into account whether the RDD is checkpointed or not.
Get the list of dependencies of this RDD, taking into account whether the RDD is checkpointed or not.
Return a new RDD containing the distinct elements in this RDD.
Return a new RDD containing the distinct elements in this RDD.
Performs a relational except on two SchemaRDDs
Performs a relational except on two SchemaRDDs
the SchemaRDD that should be excepted from this one.
Return a new RDD containing only the elements that satisfy a predicate.
Return the first element in this RDD.
Return the first element in this RDD.
Returns the first parent RDD
Returns the first parent RDD
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
Return a new RDD by first applying a function to all elements of this RDD, and then flattening the results.
Aggregate the elements of each partition, and then the results for all the partitions, using a given associative function and a neutral "zero value".
Aggregate the elements of each partition, and then the results for all the partitions, using a given associative 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.
Applies a function f to all elements of this RDD.
Applies a function f to all elements of this RDD.
Applies a function f to each partition of this RDD.
Applies a function f to each partition of this RDD.
:: Experimental :: Applies the given Generator, or table generating function, to this relation.
:: Experimental :: Applies the given Generator, or table generating function, to this relation.
A table generating function. The API for such functions is likely to change in future releases
when set to true, each output row of the generator is joined with the input row that produced it.
when set to true, at least one row will be produced for each input row, similar to
an OUTER JOIN in SQL. When no output rows are produced by the generator for a
given row, a single row will be output, with NULL values for each of the
generated columns.
an optional alias that can be used as qualifier for the attributes that are produced by this generate operation.
Gets the name of the file to which this RDD was checkpointed
Gets the name of the file to which this RDD was checkpointed
Implemented by subclasses to return how this RDD depends on parent RDDs.
Implemented by subclasses to return the set of partitions in this RDD.
Optionally overridden by subclasses to specify placement preferences.
Optionally overridden by subclasses to specify placement preferences.
Get the RDD's current storage level, or StorageLevel.
Get the RDD's current storage level, or StorageLevel.NONE if none is set.
Return an RDD created by coalescing all elements within each partition into an array.
Return an RDD created by coalescing all elements within each partition into an array.
Performs a grouping followed by an aggregation.
Performs a grouping followed by an aggregation.
schemaRDD.groupBy('year)(Sum('sales) as 'totalSales)Return an RDD of grouped items.
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.
Return an RDD of grouped elements.
Return an RDD of grouped elements. 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.
Return an RDD of grouped items.
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.
A unique ID for this RDD (within its SparkContext).
A unique ID for this RDD (within its SparkContext).
:: Experimental :: Appends the rows from this RDD to the specified table.
:: Experimental :: Appends the rows from this RDD to the specified table.
:: Experimental :: Adds the rows from this RDD to the specified table, optionally overwriting the existing data.
:: Experimental :: Adds the rows from this RDD to the specified table, optionally overwriting the existing data.
Performs a relational intersect on two SchemaRDDs
Performs a relational intersect on two SchemaRDDs
the SchemaRDD that should be intersected with this one.
Return the intersection of this RDD and another one.
Return the intersection of this RDD and another one. The output will not contain any duplicate elements, even if the input RDDs did. Performs a hash partition across the cluster
Note that this method performs a shuffle internally.
How many partitions to use in the resulting RDD
Return the intersection of this RDD and another one.
Return the intersection of this RDD and another one.
Return whether this RDD has been checkpointed or not
Return whether this RDD has been checkpointed or not
Internal method to this RDD; will read from cache if applicable, or otherwise compute it.
Internal method to this RDD; will read from cache if applicable, or otherwise compute it. This should not be called by users directly, but is available for implementors of custom subclasses of RDD.
Performs a relational join on two SchemaRDDs
Performs a relational join on two SchemaRDDs
the SchemaRDD that should be joined with this one.
One of Inner, LeftOuter, RightOuter, or FullOuter. Defaults to Inner.
An optional condition for the join operation. This is equivalent to the ON
clause in standard SQL. In the case of Inner joins, specifying a
condition is equivalent to adding where clauses after the join.
Creates tuples of the elements in this RDD by applying f.
Creates tuples of the elements in this RDD by applying f.
Limits the results by the given integer.
Limits the results by the given integer.
schemaRDD.limit(10)Return a new RDD by applying a function to all elements of this RDD.
Return a new RDD by applying a function to all elements of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
Return a new RDD by applying a function to each partition of this RDD.
preservesPartitioning indicates whether the input function preserves the partitioner, which
should be false unless this is a pair RDD and the input function doesn't modify the keys.
Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition.
Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition.
preservesPartitioning indicates whether the input function preserves the partitioner, which
should be false unless this is a pair RDD and the input function doesn't modify the keys.
Returns the max of this RDD as defined by the implicit Ordering[T].
Returns the max of this RDD as defined by the implicit Ordering[T].
the maximum element of the RDD
Returns the min of this RDD as defined by the implicit Ordering[T].
Returns the min of this RDD as defined by the implicit Ordering[T].
the minimum element of the RDD
A friendly name for this RDD
A friendly name for this RDD
Sorts the results by the given expressions.
Sorts the results by the given expressions.
schemaRDD.orderBy('a)
schemaRDD.orderBy('a, 'b)
schemaRDD.orderBy('a.asc, 'b.desc)Returns the jth parent RDD: e.
Returns the jth parent RDD: e.g. rdd.parent[T](0) is equivalent to rdd.firstParent[T]
Optionally overridden by subclasses to specify how they are partitioned.
Optionally overridden by subclasses to specify how they are partitioned.
Get the array of partitions of this RDD, taking into account whether the RDD is checkpointed or not.
Get the array of partitions of this RDD, taking into account whether the RDD is checkpointed or not.
Set this RDD's storage level to persist its values across operations after the first time it is computed.
Persist this RDD with the default storage level (MEMORY_ONLY).
Persist this RDD with the default storage level (MEMORY_ONLY).
Return an RDD created by piping elements to a forked external process.
Return an RDD created by piping elements to a forked external process. The print behavior can be customized by providing two functions.
command to run in forked process.
environment variables to set.
Before piping elements, this function is called as an oppotunity to pipe context data. Print line function (like out.println) will be passed as printPipeContext's parameter.
Use this function to customize how to pipe elements. This function will be called with each RDD element as the 1st parameter, and the print line function (like out.println()) as the 2nd parameter. An example of pipe the RDD data of groupBy() in a streaming way, instead of constructing a huge String to concat all the elements: def printRDDElement(record:(String, Seq[String]), f:String=>Unit) = for (e <- record._2){f(e)}
Use separate working directories for each task.
the result RDD
Return an RDD created by piping elements to a forked external process.
Return an RDD created by piping elements to a forked external process.
Return an RDD created by piping elements to a forked external process.
Return an RDD created by piping elements to a forked external process.
Get the preferred locations of a partition, taking into account whether the RDD is checkpointed.
Get the preferred locations of a partition, taking into account whether the RDD is checkpointed.
Prints out the schema.
Prints out the schema.
:: DeveloperApi :: A lazily computed query execution workflow.
:: DeveloperApi :: A lazily computed query execution workflow. All other RDD operations are passed through to the RDD that is produced by this workflow. This workflow is produced lazily because invoking the whole query optimization pipeline can be expensive.
The query execution is considered a Developer API as phases may be added or removed in future releases. This execution is only exposed to provide an interface for inspecting the various phases for debugging purposes. Applications should not depend on particular phases existing or producing any specific output, even for exactly the same query.
Additionally, the RDD exposed by this execution is not designed for consumption by end users. In particular, it does not contain any schema information, and it reuses Row objects internally. This object reuse improves performance, but can make programming against the RDD more difficult. Instead end users should perform RDD operations on a SchemaRDD directly.
Randomly splits this RDD with the provided weights.
Randomly splits this RDD with the provided weights.
weights for splits, will be normalized if they don't sum to 1
random seed
split RDDs in an array
Reduces the elements of this RDD using the specified commutative and associative binary operator.
Reduces the elements of this RDD using the specified commutative and associative binary operator.
Registers this RDD as a temporary table using the given name.
Registers this RDD as a temporary table using the given name. The lifetime of this temporary table is tied to the SQLContext that was used to create this SchemaRDD.
Return a new RDD that has exactly numPartitions partitions.
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.
:: Experimental :: Returns a sampled version of the underlying dataset.
:: Experimental :: Returns a sampled version of the underlying dataset.
Save this RDD as a SequenceFile of serialized objects.
Save this RDD as a SequenceFile of serialized objects.
Saves the contents of this SchemaRDD as a parquet file, preserving the schema.
Saves the contents of this SchemaRDD as a parquet file, preserving the schema. Files that
are written out using this method can be read back in as a SchemaRDD using the parquetFile
function.
:: Experimental :: Creates a table from the the contents of this SchemaRDD.
:: Experimental :: Creates a table from the the contents of this SchemaRDD. This will fail if the table already exists.
Note that this currently only works with SchemaRDDs that are created from a HiveContext as
there is no notion of a persisted catalog in a standard SQL context. Instead you can write
an RDD out to a parquet file, and then register that file as a table. This "table" can then
be the target of an insertInto.
Save this RDD as a compressed text file, using string representations of elements.
Save this RDD as a compressed text file, using string representations of elements.
Save this RDD as a text file, using string representations of elements.
Save this RDD as a text file, using string representations of elements.
Returns the schema of this SchemaRDD (represented by a StructType).
Returns the schema as a string in the tree format.
Returns the schema as a string in the tree format.
Changes the output of this relation to the given expressions, similar to the SELECT clause
in SQL.
Changes the output of this relation to the given expressions, similar to the SELECT clause
in SQL.
schemaRDD.select('a, 'b + 'c, 'd as 'aliasedName)
a set of logical expression that will be evaluated for each input row.
Assign a name to this RDD
Assign a name to this RDD
Return this RDD sorted by the given key function.
Return this RDD sorted by the given key function.
The SparkContext that created this RDD.
The SparkContext that created this RDD.
Return an RDD with the elements from this that are not in other.
Return an RDD with the elements from this that are not in other.
Return an RDD with the elements from this that are not in other.
Take the first num elements of the RDD.
Returns the first k (smallest) elements from this RDD as defined by the specified implicit Ordering[T] and maintains the ordering.
Returns the first k (smallest) elements from this RDD as defined by the specified implicit Ordering[T] and maintains the ordering. This does the opposite of top. For example:
sc.parallelize(Seq(10, 4, 2, 12, 3)).takeOrdered(1) // returns Array(2) sc.parallelize(Seq(2, 3, 4, 5, 6)).takeOrdered(2) // returns Array(2, 3)
k, the number of elements to return
the implicit ordering for T
an array of top elements
Return a fixed-size sampled subset of this RDD in an array
Return a fixed-size sampled subset of this RDD in an array
whether sampling is done with replacement
size of the returned sample
seed for the random number generator
sample of specified size in an array
A description of this RDD and its recursive dependencies for debugging.
A description of this RDD and its recursive dependencies for debugging.
Returns a new RDD with each row transformed to a JSON string.
Returns this RDD as a JavaSchemaRDD.
Return an iterator that contains all of the elements in this RDD.
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.
Returns this RDD as a SchemaRDD.
Returns this RDD as a SchemaRDD. Intended primarily to force the invocation of the implicit conversion from a standard RDD to a SchemaRDD.
Returns the top k (largest) elements from this RDD as defined by the specified implicit Ordering[T].
Returns the top k (largest) elements from this RDD as defined by the specified implicit Ordering[T]. This does the opposite of takeOrdered. For example:
sc.parallelize(Seq(10, 4, 2, 12, 3)).top(1) // returns Array(12) sc.parallelize(Seq(2, 3, 4, 5, 6)).top(2) // returns Array(6, 5)
k, the number of top elements to return
the implicit ordering for T
an array of top elements
Return the union of this RDD and another one.
Return the union of this RDD and another one. Any identical elements will appear multiple
times (use .distinct() to eliminate them).
Combines the tuples of two RDDs with the same schema, keeping duplicates.
Mark the RDD as non-persistent, and remove all blocks for it from memory and disk.
:: Experimental ::
Filters tuples using a function over a Dynamic version of a given Row.
:: Experimental ::
Filters tuples using a function over a Dynamic version of a given Row. DynamicRows use
scala's Dynamic trait to emulate an ORM of in a dynamically typed language. Since the type of
the column is not known at compile time, all attributes are converted to strings before
being passed to the function.
schemaRDD.where(r => r.firstName == "Bob" && r.lastName == "Smith")
Filters tuples using a function over the value of the specified column.
Filters tuples using a function over the value of the specified column.
schemaRDD.where('a)((a: Int) => ...)Filters the output, only returning those rows where condition evaluates to true.
Filters the output, only returning those rows where condition evaluates to true.
schemaRDD.where('a === 'b) schemaRDD.where('a === 1) schemaRDD.where('a + 'b > 10)
Zips this RDD with another one, returning key-value pairs with the first element in each RDD, second element in each RDD, etc.
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).
Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD by applying a function to the zipped partitions.
Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD by applying a function to the zipped partitions. Assumes that all the RDDs have the *same number of partitions*, but does *not* require them to have the same number of elements in each partition.
Zips this RDD with its element indices.
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 is similar to Scala's zipWithIndex but it uses Long instead of Int as the index type. This method needs to trigger a spark job when this RDD contains more than one partitions.
Note that some RDDs, such as those returned by groupBy(), do not guarantee order of elements in a partition. The index assigned to each element is therefore not guaranteed, and may even change if the RDD is reevaluated. If a fixed ordering is required to guarantee the same index assignments, you should sort the RDD with sortByKey() or save it to a file.
Zips this RDD with generated unique Long ids.
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 org.apache.spark.rdd.RDD#zipWithIndex.
Note that some RDDs, such as those returned by groupBy(), do not guarantee order of elements in a partition. The unique ID assigned to each element is therefore not guaranteed, and may even change if the RDD is reevaluated. If a fixed ordering is required to guarantee the same index assignments, you should sort the RDD with sortByKey() or save it to a file.
Filters this RDD with p, where p takes an additional parameter of type A.
Filters this RDD with p, where p takes an additional parameter of type A. This additional parameter is produced by constructA, which is called in each partition with the index of that partition.
(Since version 1.0.0) use mapPartitionsWithIndex and filter
FlatMaps f over this RDD, where f takes an additional parameter of type A.
FlatMaps f over this RDD, where f takes an additional parameter of type A. This additional parameter is produced by constructA, which is called in each partition with the index of that partition.
(Since version 1.0.0) use mapPartitionsWithIndex and flatMap
Applies f to each element of this RDD, where f takes an additional parameter of type A.
Applies f to each element of this RDD, where f takes an additional parameter of type A. This additional parameter is produced by constructA, which is called in each partition with the index of that partition.
(Since version 1.0.0) use mapPartitionsWithIndex and foreach
(Since version 1.1.0) use limit with integer argument
:: DeveloperApi :: Return a new RDD by applying a function to each partition of this RDD.
:: DeveloperApi :: Return a new RDD by applying a function to each partition of this RDD. This is a variant of mapPartitions that also passes the TaskContext into the closure.
preservesPartitioning indicates whether the input function preserves the partitioner, which
should be false unless this is a pair RDD and the input function doesn't modify the keys.
(Since version 1.2.0) use TaskContext.get
Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition.
Return a new RDD by applying a function to each partition of this RDD, while tracking the index of the original partition.
(Since version 0.7.0) use mapPartitionsWithIndex
Maps f over this RDD, where f takes an additional parameter of type A.
Maps f over this RDD, where f takes an additional parameter of type A. This additional parameter is produced by constructA, which is called in each partition with the index of that partition.
(Since version 1.0.0) use mapPartitionsWithIndex
(Since version 1.1) Use registerTempTable instead of registerAsTable.
Return an array that contains all of the elements in this RDD.
Return an array that contains all of the elements in this RDD.
(Since version 1.0.0) use collect
Functions that create new queries from SchemaRDDs. The result of all query functions is also a SchemaRDD, allowing multiple operations to be chained using a builder pattern.
:: AlphaComponent :: An RDD of Row objects that has an associated schema. In addition to standard RDD functions, SchemaRDDs can be used in relational queries, as shown in the examples below.
Importing a SQLContext brings an implicit into scope that automatically converts a standard RDD whose elements are scala case classes into a SchemaRDD. This conversion can also be done explicitly using the
createSchemaRDDfunction on a SQLContext.A
SchemaRDDcan also be created by loading data in from external sources. Examples are loading data from Parquet files by using theparquetFilemethod on SQLContext and loading JSON datasets by usingjsonFileandjsonRDDmethods on SQLContext.SQL Queries
A SchemaRDD can be registered as a table in the SQLContext that was used to create it. Once an RDD has been registered as a table, it can be used in the FROM clause of SQL statements.
Language Integrated Queries