I have grouped all my customers in JavaPairRDD<Long, Iterable<ProductBean>> by there customerId (of Long type). Means every customerId have a List or ProductBean.
Now i want to save all ProductBean to DB irrespective of customerId. I got all values by using method
JavaRDD<Iterable<ProductBean>> values = custGroupRDD.values();
Now i want to convert JavaRDD<Iterable<ProductBean>> to JavaRDD<Object, BSONObject> so that i can save it to Mongo. Remember, every BSONObject is made of Single ProductBean.
I am not getting any idea of how to do this in Spark, i mean which Spark's Transformation is used to do that job. I think this task is some kind of seperate all values from Iterable. Please let me know how is this possible.
Any hint in Scala or Python are also ok.
You can use the flatMapValues function:
JavaRDD<Object,ProductBean> result = custGroupRDD.flatMapValues(v -> v)
Related
I have a scala dataframe with two columns:
id: String
updated: Timestamp
From this dataframe I just want to get out the latest date, for which I use the following code at the moment:
df.agg(max("updated")).head()
// returns a row
I've just read about the collect() function, which I'm told to be
safer to use for such a problem - when it runs as a job, it appears it is not aggregating the max on the whole dataset, it looks perfectly fine when it is running in a notebook -, but I don't understand how it should
be used.
I found an implementation like the following, but I could not figure how it should be used...
df1.agg({"x": "max"}).collect()[0]
I tried it like the following:
df.agg(max("updated")).collect()(0)
Without (0) it returns an Array, which actually looks good. So idea is, we should apply the aggregation on the whole dataset loaded in the drive, not just the partitioned version, otherwise it seems to not retrieve all the timestamps. My question now is, how is collect() actually supposed to work in such a situation?
Thanks a lot in advance!
I'm assuming that you are talking about a spark dataframe (not scala).
If you just want the latest date (only that column) you can do:
df.select(max("updated"))
You can see what's inside the dataframe with df.show(). Since df are immutable you need to assign the result of the select to another variable or add the show after the select().
This will return a dataframe with just one row with the max value in "updated" column.
To answer to your question:
So idea is, we should apply the aggregation on the whole dataset loaded in the drive, not just the partitioned version, otherwise it seems to not retrieve all the timestamp
When you select on a dataframe, spark will select data from the whole dataset, there is not a partitioned version and a driver version. Spark will shard your data across your cluster and all the operations that you define will be done on the entire dataset.
My question now is, how is collect() actually supposed to work in such a situation?
The collect operation is converting from a spark dataframe into an array (which is not distributed) and the array will be in the driver node, bear in mind that if your dataframe size exceed the memory available in the driver you will have an outOfMemoryError.
In this case if you do:
df.select(max("Timestamp")).collect().head
You DF (that contains only one row with one column which is your date), will be converted to a scala array. In this case is safe because the select(max()) will return just one row.
Take some time to read more about spark dataframe/rdd and the difference between transformation and action.
It sounds weird. First of all you don´t need to collect the dataframe to get the last element of a sorted dataframe. There are many answers to this topics:
How to get the last row from DataFrame?
I have an RDD of objects that I want to bulk delete from HBase. After reading HBase documentation and examples I came up with the following code:
hc.bulkDelete[Array[Byte]](salesObjects, TableName.valueOf("salesInfo"),
putRecord => new Delete(putRecord), 4)
However as far as I understand salesObjects has to be converted to Array[Byte].
Since salesObjects is an RDD[Sale] how to convert it to Array[Byte] correctly?
I've tried Bytes.toBytes(salesObjects) but the method doesn't accept RDD[Sale] as an argument. Sale is a complex object so it will be problematic to parse each field to bytes.
For now I've converted RDD[Sale] to val salesList: List[Sale] = salesObjects.collect().toList but currently stuck with where to proceed next.
I've never used this method but I'll try to help:
the methods accepts a RDD of any type T: https://github.com/apache/hbase/blob/master/hbase-spark/src/main/scala/org/apache/hadoop/hbase/spark/HBaseContext.scala#L290 ==> so you should be able to use it on your RDD[Sale]
bulkDelete expects a function transforming your Sale object to HBase's Delete object (https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Delete.html)
Delete object represents a row to delete. You can get an example of Delete object initialization here: https://www.tutorialspoint.com/hbase/hbase_delete_data.htm
depending on what and how you want to remove a row, you should convert the parts of your Sales into a byte. For instance, you want to remove the data by row key, you should extract it and put into Delete object
In my understanding bulkDelete method will accumulate batchSize number of Delete objects and send them into HBase at once. Otherwise, could you please show some code to get a more concrete idea of what you're trying to do ?
Doing val salesList: List[Sale] = salesObjects.collect().toList is not a good idea since it brings all data into your driver. Potentially it can lead to OOM problems.
I have two dataframes, both of them contain different number of columns.
I need to compare three fields between them to check if those are equal.
I tried following approach but its not working.
if(df_table_stats("rec_cnt").equals(df_aud("REC_CNT")) || df_table_stats("hashcount").equals(df_aud("HASH_CNT")) || round(df_table_stats("hashsum"),0).equals(round(df_aud("HASH_TTL"),0)))
{
println("Job executed succefully")
}
df_table_stats("rec_cnt"), this returns Column rather than actual value hence condition becoming false.
Also, please explain difference between df_table_stats.select("rec_cnt") and df_table_stats("rec_cnt").
Thanks.
Use sql and inner join both df , with your conditions .
Per my comment, the syntax you're using are simple column references, they don't actually return data. Assuming you MUST use Spark for this, you'd want a method that actually returns the data, known in Spark as an action. For this case you can use take to return the first Row of data and extract the desired columns:
val tableStatsRow: Row = df_table_stats.take(1).head
val audRow: Row = df_aud.take(1).head
val tableStatsRecCount = tableStatsRow.getAs[Int]("rec_cnt")
val audRecCount = audRow.getAs[Int]("REC_CNT")
//repeat for the other values you need to capture
However, Spark definitely is overkill if this is all you're using it for. You could use a simple JDBC library for Scala like ScalikeJDBC to do these queries and capture the primitives in the results.
I am trying to retrieve the value of a DataFrame column and store it in a variable. I tried this :
val name=df.select("name")
val name1=name.collect()
But none of the above is returning the value of column "name".
Spark version :2.2.0
Scala version :2.11.11
There are couple of things here. If you want see all the data collect is the way to go. However in case your data is too huge it will cause drive to fail.
So the alternate is to check few items from the dataframe. What I generally do is
df.limit(10).select("name").as[String].collect()
This will provide output of 10 element. But now the output doesn't look good
So, 2nd alternative is
df.select("name").show(10)
This will print first 10 element, Sometime if the column values are big it generally put "..." instead of actual value which is annoying.
Hence there is third option
df.select("name").take(10).foreach(println)
Takes 10 element and print them.
Now in all the cases you won't get a fair sample of the data, as the first 10 data will be picked. So to truely pickup randomly from the dataframe you can use
df.select("name").sample(.2, true).show(10)
or
df.select("name").sample(.2, true).take(10).foreach(println)
You can check the "sample" function on dataframe
The first will do :)
val name = df.select("name") will return another DataFrame. You can do for example name.show() to show content of the DataFrame. You can also do collect or collectAsMap to materialize results on driver, but be aware, that data amount should not be too big for driver
You can also do:
val names = df.select("name").as[String].collect()
This will return array of names in this DataFrame
I have a difficulty when working with data frames in spark with Scala. If I have a data frame that I want to extract a column of unique entries, when I use groupBy I don't get a data frame back.
For example, I have a DataFrame called logs that has the following form:
machine_id | event | other_stuff
34131231 | thing | stuff
83423984 | notathing | notstuff
34131231 | thing | morestuff
and I would like the unique machine ids where event is thing stored in a new DataFrame to allow me to do some filtering of some kind. Using
val machineId = logs
.where($"event" === "thing")
.select("machine_id")
.groupBy("machine_id")
I get a val of Grouped Data back which is a pain in the butt to use (or I don't know how to use this kind of object properly). Having got this list of unique machine id's, I then want to use this in filtering another DataFrame to extract all events for individual machine ids.
I can see I'll want to do this kind of thing fairly regularly and the basic workflow is:
Extract unique id's from a log table.
Use unique ids to extract all events for a particular id.
Use some kind of analysis on this data that has been extracted.
It's the first two steps I would appreciate some guidance with here.
I appreciate this example is kind of contrived but hopefully it explains what my issue is. It may be I don't know enough about GroupedData objects or (as I'm hoping) I'm missing something in data frames that makes this easy. I'm using spark 1.5 built on Scala 2.10.4.
Thanks
Just use distinct not groupBy:
val machineId = logs.where($"event"==="thing").select("machine_id").distinct
Which will be equivalent to SQL:
SELECT DISTINCT machine_id FROM logs WHERE event = 'thing'
GroupedData is not intended to be used directly. It provides a number of methods, where agg is the most general, which can be used to apply different aggregate functions and convert it back to DataFrame. In terms of SQL what you have after where and groupBy is equivalent to something like this
SELECT machine_id, ... FROM logs WHERE event = 'thing' GROUP BY machine_id
where ... has to be provided by agg or equivalent method.
A group by in spark followed by aggregation and then a select statement will return a data frame. For your example it should be something like:
val machineId = logs
.groupBy("machine_id", "event")
.agg(max("other_stuff") )
.select($"machine_id").where($"event" === "thing")