I have two hive clustered tables t1 and t2
CREATE EXTERNAL TABLE `t1`(
`t1_req_id` string,
...
PARTITIONED BY (`t1_stats_date` string)
CLUSTERED BY (t1_req_id) INTO 1000 BUCKETS
// t2 looks similar with same amount of buckets
The insert part happens in hive
set hive.exec.dynamic.partition=true;
set hive.exec.dynamic.partition.mode=nonstrict;
insert overwrite table `t1` partition(t1_stats_date,t1_stats_hour)
select *
from t1_raw
where t1_stats_date='2020-05-10' and t1_stats_hour='12' AND
t1_req_id is not null
The code looks like as following:
val t1 = spark.table("t1").as[T1]
val t2= spark.table("t2").as[T2]
val outDS = t1.joinWith(t2, t1("t1_req_id) === t2("t2_req_id), "fullouter")
.map { case (t1Obj, t2Obj) =>
val t3:T3 = // do some logic
t3
}
outDS.toDF.write....
I see projection in DAG - but it seems that the job still does full data shuffle
Also, while looking into the logs of executor I don't see it reads the same bucket of the two tables in one chunk - that what I would expect to find
There are spark.sql.sources.bucketing.enabled, spark.sessionState.conf.bucketingEnabled and
spark.sql.join.preferSortMergeJoin flags
What am I missing? and why is there still full shuffle, if there are bucketed tables?
The current spark version is 2.3.1
One possibility here to check for is if you have a type mismatch. E.g. if the type of the join column is string in T1 and BIGINT in T2. Even if the types are both integer (e.g. one is INT, another BIGINT) Spark will still add shuffle here because different types use different hash functions for bucketing.
Related
I have two hive clustered tables t1 and t2
CREATE EXTERNAL TABLE `t1`(
`t1_req_id` string,
...
PARTITIONED BY (`t1_stats_date` string)
CLUSTERED BY (t1_req_id) INTO 1000 BUCKETS
// t2 looks similar with same amount of buckets
the code looks like as following:
val t1 = spark.table("t1").as[T1].rdd.map(v => (v.t1_req_id, v))
val t2= spark.table("t2").as[T2].rdd.map(v => (v.t2_req_id, v))
val outRdd = t1.cogroup(t2)
.flatMap { coGroupRes =>
val key = coGroupRes._1
val value: (Iterable[T1], Iterable[T2])= coGroupRes._2
val t3List = // create a list with some logic on Iterable[T1] and Iterable[T2]
t3List
}
outRdd.write....
I make sure that the both t1 and t2 table has same amount of partitions, and on spark-submit there are
spark.sql.sources.bucketing.enabled=true and spark.sessionState.conf.bucketingEnabled=true flags
But Spark DAG doesn't show any impact of clustering. It seems there is still data full shuffle
What am I missing, any other configurations, tunings? How can it be assured that there is no full data shuffle?
My spark version is 2.3.1
And it shouldn't show.
Any logical optimizations are limited to DataFrame API. Once you push data to black-box functional dataset API (see Spark 2.0 Dataset vs DataFrame) and later to RDD API, no more information is pushed back to the optimizer.
You could partially utilize bucketing by performing join first, getting something around these lines
spark.table("t1")
.join(spark.table("t2"), $"t1.t1_req_id" === $"t2.t2_req_id", "outer")
.groupBy($"t1.v.t1_req_id", $"t2.t2_req_id")
.agg(...) // For example collect_set($"t1.v"), collect_set($"t2.v")
However, unlike cogroup, this will generate full Cartesian Products within groups, and might be not applicable in your case
I have five Hive tables assume the names is A, B, C, D, and E. For each table there is a customer_id as the key for join between them. Also, Each table contains at least 100:600 columns all of them is Parquet format.
Example of one table below:
CREATE TABLE table_a
(
customer_id Long,
col_1 STRING,
col_2 STRING,
col_3 STRING,
.
.
col_600 STRING
)
STORED AS PARQUET;
I need to achieve two points,
Join all of them together with the most optimum way using Spark Scala. I tried to sortByKey before join but still there is a performance bottleneck. I tried to reparation by key before join but the performance is still not good. I tried to increase the parallelism for Spark to make it 6000 with many executors but not able to achieve a good results.
After join I need to apply a separate function for some of these columns.
Sample of the join I tried below,
val dsA = spark.table(table_a)
val dsB = spark.table(table_b)
val dsC = spark.table(table_c)
val dsD = spark.table(table_d)
val dsE = spark.table(table_e)
val dsAJoineddsB = dsA.join(dsB,Seq(customer_id),"inner")
I think in this case the direct join is not the optimal case. You can acheive this task using the below simple way.
First, create case class for example FeatureData with two fields case class FeatureData(customer_id:Long, featureValue:Map[String,String])
Second, You will map each table to FeatureData case class key, [feature_name,feature_value]
Third, You will groupByKey and union all the dataset with the same key.
I the above way it will be faster to union than join. But it need more work.
After that, you will have a dataset with key,map. You will apply the transformation for key, Map(feature_name).
Simple example of the implementation as following:
You will map first the dataset to the case class then you can union all of them. After that you will groupByKey then map it and reduce it.
case class FeatureMappedData(customer_id:Long, feature: Map[String, String])
val dsAMapped = dsA.map(row ⇒
FeatureMappedData(row.customer_id,
Map("featureA" -> row.featureA,
"featureB" -> row.featureB)))
val unionDataSet = dsAMapped union dsBMapped
unionDataSet.groupByKey(_.customer_id)
.mapGroups({
case (eid, featureIter) ⇒ {
val featuresMapped: Map[String, String] = featureIter.map(_.feature).reduce(_ ++ _).withDefaultValue("0")
FeatureMappedData(customer_id, featuresMapped)
}
})
Spark 2.2.0
I have the following code converted from SQL script. It has been running for two hours and it's still running. Even slower than SQL Server. Is anything not done correctly?
The following is the plan,
Push table2 to all executors
Partition table1 and distribute the partitions to executors.
And each row in table2/t2 joins (cross join) each partition of table1.
So the calculation on the result of the cross-join can be run distributed/parallelly. (I wanted to, for example suppose I have 16 executors, keep a copy of t2 on all the 16 executors. Then divide table 1 into 16 partitions, one for each executor. Then each executor do the calculation on one partition of table 1 and t2.)
case class Cols (Id: Int, F2: String, F3: BigDecimal, F4: Date, F5: String,
F6: String, F7: BigDecimal, F8: String, F9: String, F10: String )
case class Result (Id1: Int, ID2: Int, Point: Int)
def getDataFromDB(source: String) = {
import sqlContext.sparkSession.implicits._
sqlContext.read.format("jdbc").options(Map(
"driver" -> "com.microsoft.sqlserver.jdbc.SQLServerDriver",
"url" -> jdbcSqlConn,
"dbtable" -> s"$source"
)).load()
.select("Id", "F2", "F3", "F4", "F5", "F6", "F7", "F8", "F9", "F10")
.as[Cols]
}
val sc = new SparkContext(conf)
val table1:DataSet[Cols] = getDataFromDB("table1").repartition(32).cache()
println(table1.count()) // about 300K rows
val table2:DataSet[Cols] = getDataFromDB("table2") // ~20K rows
table2.take(1)
println(table2.count())
val t2 = sc.broadcast(table2)
import org.apache.spark.sql.{functions => func}
val j = table1.joinWith(t2.value, func.lit(true))
j.map(x => {
val (l, r) = x
Result(l.Id, r.Id,
(if (l.F1!= null && r.F1!= null && l.F1== r.F1) 3 else 0)
+(if (l.F2!= null && r.F2!= null && l.F2== r.F2) 2 else 0)
+ ..... // All kind of the similiar expression
+(if (l.F8!= null && r.F8!= null && l.F8== r.F8) 1 else 0)
)
}).filter(x => x.Value >= 10)
println("Total count %d", j.count()) // This takes forever, the count will be about 100
How to rewrite it with Spark idiomatic way?
Ref: https://forums.databricks.com/questions/6747/how-do-i-get-a-cartesian-product-of-a-huge-dataset.html
(Somehow I feel as if I have seen the code already)
The code is slow because you use just a single task to load the entire dataset from the database using JDBC and despite cache it does not benefit from it.
Start by checking out the physical plan and Executors tab in web UI to find out about the single executor and the single task to do the work.
You should use one of the following to fine-tune the number of tasks for loading:
Use partitionColumn, lowerBound, upperBound options for the JDBC data source
Use predicates option
See JDBC To Other Databases in Spark's official documentation.
After you're fine with the loading, you should work on improving the last count action and add...another count action right after the following line:
val table1: DataSet[Cols] = getDataFromDB("table1").repartition(32).cache()
// trigger caching as it's lazy in Dataset API
table1.count
The reason why the entire query is slow is that you only mark table1 to be cached when an action gets executed which is exactly at the end (!) In other words, cache does nothing useful and more importantly makes the query performance even worse.
Performance will increase after you table2.cache.count too.
If you want to do cross join, use crossJoin operator.
crossJoin(right: Dataset[_]): DataFrame Explicit cartesian join with another DataFrame.
Please note the note from the scaladoc of crossJoin (no pun intended).
Cartesian joins are very expensive without an extra filter that can be pushed down.
The following requirement is already handled by Spark given all the optimizations available.
So the calculation on the result of the cross-join can be run distributed/parallelly.
That's Spark's job (again, no pun intended).
The following requirement begs for broadcast.
I wanted to, for example suppose I have 16 executors, keep a copy of t2 on all the 16 executors. Then divide table 1 into 16 partitions, one for each executor. Then each executor do the calculation on one partition of table 1 and t2.)
Use broadcast function to hint Spark SQL's engine to use table2 in broadcast mode.
broadcast[T](df: Dataset[T]): Dataset[T] Marks a DataFrame as small enough for use in broadcast joins.
I currently have a large RDD called chartEvents containing data of the form:
case class ChartEvent(patientID: String, itemID: String, chartTime: String, storeTime: String, value: String,
valueNum: String, warning: String, error: String)
The data is coming from a 35 GB .csv file which I am parsing in using SQL:
CSVUtils.loadCSVAsTable(sqlContext, "data_unzipped/CHARTEVENTS.csv")
val chartEvents = sqlContext.sql(
"""
|SELECT SUBJECT_ID, ITEMID, CHARTTIME, STORETIME, VALUE, VALUENUM, WARNING, ERROR
|FROM CHARTEVENTS
""".stripMargin)
.map(r => ChartEvent(r(0).toString, r(1).toString, r(2).toString, r(3).toString, r(4).toString,
r(5).toString, r(6).toString, r(7).toString))
I have a separate, very small (less than 100 rows) RDD called featureMapping of the form RDD[(itemID, label)] where these are both strings. What I am trying to do is filter down the chartEvents RDD to rows which only contain itemIDs in featureMapping. My current method is to perform an inner join of the two RDDs as follows:
val result = chartEvents.map{case event => (event.itemID, event)}.join(featureMapping)
However, I am noticing that this is on track to take several hours to run, and is using a massive amount of space in my /user/<user>/appdata/local/temp folder. Is there a more efficient way to perform this filtering? Would coding it into the sqlContext be faster?
If you register your tables in hive metastore you can set spark.sql.autoBroadcastJoinThreshold
from the doc:
Configures the maximum size in bytes for a table that will be
broadcast to all worker nodes when performing a join. By setting this
value to -1 broadcasting can be disabled. Note that currently
statistics are only supported for Hive Metastore tables where the
command ANALYZE TABLE COMPUTE STATISTICS noscan has been
run.
I have two rdd one rdd have just one column other have two columns to join the two RDD on key's I have add dummy value which is 0 , is there any other efficient way of doing this using join ?
val lines = sc.textFile("ml-100k/u.data")
val movienamesfile = sc.textFile("Cml-100k/u.item")
val moviesid = lines.map(x => x.split("\t")).map(x => (x(1),0))
val test = moviesid.map(x => x._1)
val movienames = movienamesfile.map(x => x.split("\\|")).map(x => (x(0),x(1)))
val shit = movienames.join(moviesid).distinct()
Edit:
Let me convert this question in SQL. Say for example I have table1 (moveid) and table2 (movieid,moviename). In SQL we write something like:
select moviename, movieid, count(1)
from table2 inner join table table1 on table1.movieid=table2.moveid
group by ....
here in SQL table1 has only one column where as table2 has two columns still the join works, same way in Spark can join on keys from both the RDD's.
Join operation is defined only on PairwiseRDDs which are quite different from a relation / table in SQL. Each element of PairwiseRDD is a Tuple2 where the first element is the key and the second is value. Both can contain complex objects as long as key provides a meaningful hashCode
If you want to think about this in a SQL-ish you can consider key as everything that goes to ON clause and value contains selected columns.
SELECT table1.value, table2.value
FROM table1 JOIN table2 ON table1.key = table2.key
While these approaches look similar at first glance and you can express one using another there is one fundamental difference. When you look at the SQL table and you ignore constraints all columns belong in the same class of objects, while key and value in the PairwiseRDD have a clear meaning.
Going back to your problem to use join you need both key and value. Arguably much cleaner than using 0 as a placeholder would be to use null singleton but there is really no way around it.
For small data you can use filter in a similar way to broadcast join:
val moviesidBD = sc.broadcast(
lines.map(x => x.split("\t")).map(_.head).collect.toSet)
movienames.filter{case (id, _) => moviesidBD.value contains id}
but if you really want SQL-ish joins then you should simply use SparkSQL.
val movieIdsDf = lines
.map(x => x.split("\t"))
.map(a => Tuple1(a.head))
.toDF("id")
val movienamesDf = movienames.toDF("id", "name")
// Add optional join type qualifier
movienamesDf.join(movieIdsDf, movieIdsDf("id") <=> movienamesDf("id"))
On RDD Join operation is only defined for PairwiseRDDs, So need to change the value to pairedRDD. Below is a sample
val rdd1=sc.textFile("/data-001/part/")
val rdd_1=rdd1.map(x=>x.split('|')).map(x=>(x(0),x(1)))
val rdd2=sc.textFile("/data-001/partsupp/")
val rdd_2=rdd2.map(x=>x.split('|')).map(x=>(x(0),x(1)))
rdd_1.join(rdd_2).take(2).foreach(println)