What's the difference between explode function and explode operator?
spark.sql.functions.explode
explode function creates a new row for each element in the given array or map column (in a DataFrame).
val signals: DataFrame = spark.read.json(signalsJson)
signals.withColumn("element", explode($"data.datapayload"))
explode creates a Column.
See functions object and the example in How to unwind array in DataFrame (from JSON)?
Dataset<Row> explode / flatMap operator (method)
explode operator is almost the explode function.
From the scaladoc:
explode returns a new Dataset where a single column has been expanded to zero or more rows by the provided function. This is similar to a LATERAL VIEW in HiveQL. All columns of the input row are implicitly joined with each value that is output by the function.
ds.flatMap(_.words.split(" "))
Please note that (again quoting the scaladoc):
Deprecated (Since version 2.0.0) use flatMap() or select() with functions.explode() instead
See Dataset API and the example in How to split multi-value column into separate rows using typed Dataset?
Despite explode being deprecated (that we could then translate the main question to the difference between explode function and flatMap operator), the difference is that the former is a function while the latter is an operator. They have different signatures, but can give the same results. That often leads to discussions what's better and usually boils down to personal preference or coding style.
One could also say that flatMap (i.e. explode operator) is more Scala-ish given how ubiquitous flatMap is in Scala programming (mainly hidden behind for-comprehension).
flatMap is much better in performance in comparison to explode as flatMap require much lesser data shuffle.
If you are processing big data (>5 GB) the performance difference could be seen evidently.
Related
I have a data like following in pairRDD, and I would like to collect a map with username as key, and sum of each list as value. The number of users is very large say 100m+, and lists are <1k in size. There are 2 choices I can think of - mapToPair and sum list with a simple for loop inside mapToPair, or flatMapValues the list to create <user, value> pairs then reduceBykey. Which is way is better?
Seq(
("user1",List(8,2,....)),
("user2",List(1,12,.....)),
...
("userN",List(99,5,...))
)
I would guess rdd.mapValues(_.sum) would be faster because you iterate over the elements once instead of twice (once to flatten, once to reduce).
But the best answer would be to just test it an see.
Best tip I can think of though, is try to work with DataFrames or Datasets (Spark SQL) to begin with. If you end up with a flattened DataFrame you can call df.groupBy($"user").agg(F.sum($"value")) or if you have a Dataframe like the RDD you described you can just use the aggregate SQL function
i have a RDD type like this: RDD[((String), SomeDTO)]
this RDD is come from an union method, and I can be sure that the element value of the same key must be the same, so if i want distinct all element of the rdd, what is the difference between the two methods I use
\\first
context.union(Array(rdd1, rdd2)).distinct()
\\second
context.union(Array(rdd1, rdd2)).reduceByKey((_, curr) => curr)
i'm beginner of spark, the only different i know is that distinct() running slowly
Referring the source code https://github.com/apache/spark/blob/5d45a415f3a29898d92380380cfd82bfc7f579ea/core/src/main/scala/org/apache/spark/rdd/RDD.scala#L449 , distinct also follows the reduceByKey approach so you should be alright. distinct would not be slower than reduceByKey.
I have a large spark table containing mixed data types String,arrays,maps
The array and map columns are sparse in nature. Should i keep empty arrays in values for these columns or make them null?
Similarly is it recommended to use empty strings "" for storing or null?
What is a good practice and advantages and disadvantages of both?
Generally speaking I would always try to use NULL values instead of empty strings or arrays. The main reason for me for me his how they are handled in spark, e.g. when joining two data frames. NULL values are ignored in joins, but empty strings or lists are not. This can often result in very skew data, which can heavily slow down your transformations. Some information about skew data can be found here [external link].
In addition, NULL values are also often ignored in functions like coalesce of columns [docs], count in aggregations [related question] or first(col, ignorenulls=True) [docs]. If you want to use the functions as they are intended, I would also recommend using NULL over empty string/list.
To sum this up: using NULL over other values like empty strings or lists, allows you to profit for more native Spark functionality and I would recommend to use NULL when ever possible.
I am working with a legacy Spark SQL code like this:
SELECT
column1,
max(column2),
first_value(column3),
last_value(column4)
FROM
tableA
GROUP BY
column1
ORDER BY
columnN
I am rewriting it in PySpark as below
df.groupBy(column1).agg(max(column2), first(column3), last(column4)).orderBy(columnN)
When I'm comparing the two outcomes I can see differences in the fields generated by the first_value/first and last_value/last functions.
Are they behaving in a non-deterministic way when used outside of Window functions?
Can groupBy aggregates be combined with Window functions?
This behaviour is possible when you have a wide table and you don't specify ordering for the remaining columns. What happens under the hood is that spark takes first() or last() row, whichever is available to it as the first condition-matching row on the heap. Spark SQL and pyspark might access different elements because the ordering is not specified for the remaining columns.
In terms of Window function, you can use a partitionBy(f.col('column_name')) in your Window, which kind of works like a groupBy - it groups the data according to a partitioning column. However, without specifying the ordering for all columns, you might arrive at the same problem of non-determinicity. Hope this helps!
For completeness sake, I recommend you have a look at the pyspark doc for the first() and last() functions here: https://spark.apache.org/docs/2.4.3/api/python/pyspark.sql.html#pyspark.sql.functions.first
In particular, the following note brings light to why you behaviour was non-deterministic:
Note The function is non-deterministic because its results depends on order of rows which may be non-deterministic after a shuffle.
Definitely !
import pyspark.sql.functions as F
partition = Window.partitionBy("column1").orderBy("columnN")
data = data.withColumn("max_col2", F.max(F.col("column2")).over(partition))\
.withColumn("first_col3", F.first(F.col("column3")).over(partition))\
.withColumn("last_col4", F.last(F.col("column4")).over(partition))
data.show(10, False)
I have a dataset of (user, product, review), and want to feed it into mllib's ALS algorithm.
The algorithm needs users and products to be numbers, while mine are String usernames and String SKUs.
Right now, I get the distinct users and SKUs, then assign numeric IDs to them outside of Spark.
I was wondering whether there was a better way of doing this. The one approach I've thought of is to write a custom RDD that essentially enumerates 1 through n, then call zip on the two RDDs.
Starting with Spark 1.0 there are two methods you can use to solve this easily:
RDD.zipWithIndex is just like Seq.zipWithIndex, it adds contiguous (Long) numbers. This needs to count the elements in each partition first, so your input will be evaluated twice. Cache your input RDD if you want to use this.
RDD.zipWithUniqueId also gives you unique Long IDs, but they are not guaranteed to be contiguous. (They will only be contiguous if each partition has the same number of elements.) The upside is that this does not need to know anything about the input, so it will not cause double-evaluation.
For a similar example use case, I just hashed the string values. See http://blog.cloudera.com/blog/2014/03/why-apache-spark-is-a-crossover-hit-for-data-scientists/
def nnHash(tag: String) = tag.hashCode & 0x7FFFFF
var tagHashes = postIDTags.map(_._2).distinct.map(tag =>(nnHash(tag),tag))
It sounds like you're already doing something like this, although hashing can be easier to manage.
Matei suggested here an approach to emulating zipWithIndex on an RDD, which amounts to assigning IDs within each partiition that are going to be globally unique: https://groups.google.com/forum/#!topic/spark-users/WxXvcn2gl1E
Another easy option, if using DataFrames and just concerned about the uniqueness is to use function MonotonicallyIncreasingID
import org.apache.spark.sql.functions.monotonicallyIncreasingId
val newDf = df.withColumn("uniqueIdColumn", monotonicallyIncreasingId)
Edit: MonotonicallyIncreasingID was deprecated and removed since Spark 2.0; it is now known as monotonically_increasing_id .
monotonically_increasing_id() appears to be the answer, but unfortunately won't work for ALS since it produces 64-bit numbers and ALS expects 32-bit ones (see my comment below radek1st's answer for deets).
The solution I found is to use zipWithIndex(), as mentioned in Darabos' answer. Here's how to implement it:
If you already have a single-column DataFrame with your distinct users called userids, you can create a lookup table (LUT) as follows:
# PySpark code
user_als_id_LUT = sqlContext.createDataFrame(userids.rdd.map(lambda x: x[0]).zipWithIndex(), StructType([StructField("userid", StringType(), True),StructField("user_als_id", IntegerType(), True)]))
Now you can:
Use this LUT to get ALS-friendly integer IDs to provide to ALS
Use this LUT to do a reverse-lookup when you need to go back from ALS ID to the original ID
Do the same for items, obviously.
People have already recommended monotonically_increasing_id(), and mentioned the problem that it creates Longs, not Ints.
However, in my experience (caveat - Spark 1.6) - if you use it on a single executor (repartition to 1 before), there is no executor prefix used, and the number can be safely cast to Int. Obviously, you need to have less than Integer.MAX_VALUE rows.