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update row value using another table in spark java

I have 2 datasets in java spark, first one contains id, name , age
and second one are the same, i need to check values (name and id) and if it's similar update the age with new age in dataset2
i tried all possible ways but i found that java in spark don't have much resourses and all possible ways not worked
this is one i tried :
dataset1.createOrReplaceTempView("updatesTable");
datase2.createOrReplaceTempView("carsTheftsFinal2");
updatesNew.show();
Dataset<Row> test = spark.sql( "ALTER carsTheftsFinal2 set carsTheftsFinal2.age = updatesTable.age from updatesTable where carsTheftsFinal2.id = updatesTable.id AND carsTheftsFinal2.name = updatesTable.name ");
test.show(12);
and this is the error :
Exception in thread "main"
org.apache.spark.sql.catalyst.parser.ParseException: no viable
alternative at input 'ALTER carsTheftsFinal2'(line 1, pos 6)
I have hint: that i can use join to update without using update statement ( java spark not provide update )

Assume that we have ds1 with this data:
+---+-----------+---+
| id| name|age|
+---+-----------+---+
| 1| Someone| 18|
| 2| Else| 17|
| 3|SomeoneElse| 14|
+---+-----------+---+
and ds2 with this data:
+---+----------+---+
| id| name|age|
+---+----------+---+
| 1| Someone| 14|
| 2| Else| 18|
| 3|NotSomeone| 14|
+---+----------+---+
According to your expected result, the final table would be:
+---+-----------+---+
| id| name|age|
+---+-----------+---+
| 3|SomeoneElse| 14| <-- not modified, same as ds
| 1| Someone| 14| <-- modified, was 18
| 2| Else| 18| <-- modified, was 17
+---+-----------+---+
This is achieved with the following transformations, first, we rename ds2's age with age2.
val renamedDs2 = ds2.withColumnRenamed("age", "age2")
Then:
// we join main dataset with the renamed ds2, now we have age and age2
ds1.join(renamedDs2, Seq("id", "name"), "left")
// we overwrite age, if age2 is not null from ds2, take it, otherwise leave age
.withColumn("age",
when(col("age2").isNotNull, col("age2")).otherwise(col("age"))
)
// finally, we drop age2
.drop("age2")
Hope this does what you want!

Iterating rows of a Spark Dataset and applying operations in Java API

New to Spark (2.4.x) and using the Java API (not Scala!!!)
I have a Dataset that I've read in from a CSV file. It has a schema (named columns) like so:
id (integer) | name (string) | color (string) | price (double) | enabled (boolean)
An example row:
23 | "hotmeatballsoup" | "blue" | 3.95 | true
There are many (tens of thousands) rows in the dataset. I would like to write an expression using the proper Java/Spark API, that scrolls through each row and applies the following two operations on each row:
If the price is null, default it to 0.00; and then
If the color column value is "red", add 2.55 to the price
Since I'm so new to Spark I'm not sure even where to begin! My best attempt thus far is definitely wrong, but its a least a starting point I guess:
Dataset csvData = sparkSession.read()
.format("csv")
.load(fileToLoad.getAbsolutePath());
// ??? get rows somehow
Seq<Seq<String>> csvRows = csvData.getRows(???, ???);
// now how to loop through rows???
for (Seq<String> row : csvRows) {
// how apply two operations specified above???
if (row["price"] == null) {
row["price"] = 0.00;
}
if (row["color"].equals("red")) {
row["price"] = row["price"] + 2.55;
}
}
Can someone help nudge me in the right direction here?

You could use spark sql api to achieve it. Null values could also be replaced with values using .fill() from DataFrameNaFunctions. Otherwise you could convert Dataframe to Dataset and do these steps in .map, but sql api is better and more efficient in this case.
+---+---------------+-----+-----+-------+
| id| name|color|price|enabled|
+---+---------------+-----+-----+-------+
| 23|hotmeatballsoup| blue| 3.95| true|
| 24| abc| red| 1.0| true|
| 24| abc| red| null| true|
+---+---------------+-----+-----+-------+
import sql functions before class declaration:
import static org.apache.spark.sql.functions.*;
sql api:
df.select(
col("id"), col("name"), col("color"),
when(col("color").equalTo("red").and(col("price").isNotNull()), col("price").plus(2.55))
.when(col("color").equalTo("red").and(col("price").isNull()), 2.55)
.otherwise(col("price")).as("price")
,col("enabled")
).show();
or using temp view and sql query:
df.createOrReplaceTempView("df");
spark.sql("select id,name,color, case when color = 'red' and price is not null then (price + 2.55) when color = 'red' and price is null then 2.55 else price end as price, enabled from df").show();
output:
+---+---------------+-----+-----+-------+
| id| name|color|price|enabled|
+---+---------------+-----+-----+-------+
| 23|hotmeatballsoup| blue| 3.95| true|
| 24| abc| red| 3.55| true|
| 24| abc| red| 2.55| true|
+---+---------------+-----+-----+-------+

Elasticsearch - how to group by and count matches in an index

I have an instance of Elasticsearch running with thousands of documents. My index has 2 fields like this:
|____Type_____|__ Date_added __ |
| walking | 2018-11-27T00:00:00.000 |
| walking | 2018-11-26T00:00:00.000 |
| running | 2018-11-24T00:00:00.000 |
| running | 2018-11-25T00:00:00.000 |
| walking | 2018-11-27T04:00:00.000 |
I want to group by and count how many matches were found for the "type" field, in a certain range.
In SQL I would do something like this:
select type,
count(type)
from index
where date_added between '2018-11-20' and '2018-11-30'
group by type
I want to get something like this:
| type | count |
| running | 2 |
| walking | 3 |
I'm using the High Level Rest Client api in my project, so far my query looks like this, it's only filtering by the start and end time:
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
sourceBuilder.query(QueryBuilders
.boolQuery()
.must(QueryBuilders
.rangeQuery("date_added")
.from(start.getTime())
.to(end.getTime()))
)
);
How can I do a "group by" in the "type" field? Is it possible to do this in ElasticSearch?

That's a good start! Now you need to add a terms aggregation to your query:
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
sourceBuilder.query(QueryBuilders.boolQuery()
.must(QueryBuilders
.rangeQuery("date_added")
.from(start.getTime())
.to(end.getTime()))
)
);
// add these two lines
TermsAggregationBuilder groupBy = AggregationBuilders.terms("byType").field("type.keyword");
sourceBuilder.aggregation(groupBy);

After using Val's reply to aggregate the fields, I wanted to print the aggregations of my query together with the value of them. Here's what I did:
Terms terms = searchResponse.getAggregations().get("byType");
Collection<Terms.Bucket> buckets = (Collection<Bucket>) terms.getBuckets();
for (Bucket bucket : buckets) {
System.out.println("Type: " + bucket.getKeyAsString() + " = Count("+bucket.getDocCount()+")");
}
This is the output after running the query in an index with 2700 documents with a field called "type" and 2 different types:
Type: walking = Count(900)
Type: running = Count(1800)

Attempting to count unique users between two categories in Spark

I have a Dataset structure in Spark with two columns, one called user the other called category. Such that the table looks some what like this:
+---------------+---------------+
| user| category|
+---------------+---------------+
| garrett| syncopy|
| garrison| musictheory|
| marta| sheetmusic|
| garrett| orchestration|
| harold| chopin|
| marta| russianmusic|
| niko| piano|
| james| sheetmusic|
| manny| violin|
| charles| gershwin|
| dawson| cello|
| bob| cello|
| george| cello|
| george| americanmusic|
| bob| personalcompos|
| george| sheetmusic|
| fred| sheetmusic|
| bob| sheetmusic|
| garrison| sheetmusic|
| george| musictheory|
+---------------+---------------+
only showing top 20 rows
Each row in the table is unique but a user and category can appear multiple times. The objective is to count the number of users that two categories share. For example cello and americanmusic share a user named george and musictheory and sheetmusic share users george and garrison. The goal is to get the number of distinct users between n categories meaning that there is at most n squared edges between categories. I understand partially how to do this operation but I am struggling a little bit converting my thoughts to Spark Java.
My thinking is that I need to do a self-join on user to get a table that would be structured like this:
+---------------+---------------+---------------+
| user| category| category|
+---------------+---------------+---------------+
| garrison| musictheory| sheetmusic|
| george| musictheory| sheetmusic|
| garrison| musictheory| musictheory|
| george| musictheory| musicthoery|
| garrison| sheetmusic| musictheory|
| george| sheetmusic| musictheory|
+---------------+---------------+---------------+
The self join operation in Spark (Java code) is not difficult:
Dataset<Row> newDataset = allUsersToCategories.join(allUsersToCategories, "users");
This is getting somewhere, however I get mappings to the same category as in rows 3 and 4 in the above example and I get backwards mappings where the categories are reversed such that essentially is double counting each user interaction like in rows 5 and 6 of the above example.
What I would believe I need to do is have some sort of conditional in my join that says something along the lines of X < Y so that equal categories and duplicates get thrown away. Finally I then need to count the number of distinct rows for n squared combinations where n is the number of categories.
Could somebody please explain how to do this in Spark and specifically Spark Java since I am a little unfamiliar with the Scala syntax?
Thanks for the help.

I'm not sure if I understand your requirements correctly, but I will try to help.
According to my understanding expected result for above data should look like below. If it's not true, please let me know I will try to make requried modifications.
+--------------+--------------+-+
|_1 |_2 |
+--------------+--------------+-+
|personalcompos|sheetmusic |1|
|cello |musictheory |1|
|americanmusic |cello |1|
|cello |sheetmusic |2|
|cello |personalcompos|1|
|russianmusic |sheetmusic |1|
|americanmusic |sheetmusic |1|
|americanmusic |musictheory |1|
|musictheory |sheetmusic |2|
|orchestration |syncopy |1|
+--------------+--------------+-+
In this case you can solve your problem with below Scala code:
allUsersToCategories
.groupByKey(_.user)
.flatMapGroups{case (user, userCategories) =>
val categories = userCategories.map(uc => uc.category).toSeq
for {
c1 <- categories
c2 <- categories
if c1 < c2
} yield (c1, c2)
}
.groupByKey(x => x)
.count()
.show()
If you need symetric result you can just change if statement in flatMapGroups transformation to if c1 != c2.
Please note that in above example I used Dataset API, which for test purpose was created with below code:
case class UserCategory(user: String, category: String)
val allUsersToCategories = session.createDataset(Seq(
UserCategory("garrett", "syncopy"),
UserCategory("garrison", "musictheory"),
UserCategory("marta", "sheetmusic"),
UserCategory("garrett", "orchestration"),
UserCategory("harold", "chopin"),
UserCategory("marta", "russianmusic"),
UserCategory("niko", "piano"),
UserCategory("james", "sheetmusic"),
UserCategory("manny", "violin"),
UserCategory("charles", "gershwin"),
UserCategory("dawson", "cello"),
UserCategory("bob", "cello"),
UserCategory("george", "cello"),
UserCategory("george", "americanmusic"),
UserCategory("bob", "personalcompos"),
UserCategory("george", "sheetmusic"),
UserCategory("fred", "sheetmusic"),
UserCategory("bob", "sheetmusic"),
UserCategory("garrison", "sheetmusic"),
UserCategory("george", "musictheory")
))
I was trying to provide example in Java, but I don't have any experience with Java+Spark and it is too time consuming for me to migrate above example from Scala to Java...

I found the answer a couple of hours ago using spark sql:
Dataset<Row> connection per shared user = spark.sql("SELECT a.user as user, "
+ "a.category as categoryOne, "
+ "b.category as categoryTwo "
+ "FROM allTable as a INNER JOIN allTable as b "
+ "ON a.user = b.user AND a.user < b.user");
This will then create a Dataset with three columns user, categoryOne, and categoryTwo. Each row will be unique and will indicate when the user exists in both categories.

Spark Sql, unable to query

I have the data schema of LinkeIn account as shown below. I need to query the skills which is in the for of array, where array may contains either JAVA OR java OR Java or JAVA developer OR Java developer.
Dataset<Row> sqlDF = spark.sql("SELECT * FROM people"
+ " WHERE ARRAY_CONTAINS(skills,'Java') "
+ " OR ARRAY_CONTAINS(skills,'JAVA')"
+ " OR ARRAY_CONTAINS(skills,'Java developer') "
+ "AND ARRAY_CONTAINS(experience['description'],'Java developer')" );

df.printschema()
root
|-- skills: array (nullable = true)
| |-- element: string (containsNull = true)
df.show()
+--------------------+
| skills|
+--------------------+
| [Java, java]|
|[Java Developer, ...|
| [dev]|
+--------------------+