I have about 100 GB of time series data in Hadoop. I'd like to use Spark to grab all data from 1000 different time ranges.
I have tried this using Apache Hive by creating an extremely long SQL statement that has about 1000 'OR BETWEEN X AND Y OR BETWEEN Q AND R' statements.
I have also tried using Spark. In this technique I've created a dataframe that has the time ranges in question and loaded that into spark with:
spark_session.CreateDataFrame()
and
df.registerTempTable()
With this, I'm doing a join with the newly created timestamp dataframe and the larger set of timestamped data.
This query is taking an extremely long time and I'm wondering if there's a more efficient way to do this.
Especially if the data is not partitioned or ordered in any special way, you or Spark need to scan it all no matter what.
I would define a predicate given the set of time ranges:
import scala.collection.immutable.Range
val ranges: List[Range] = ??? // load your ranges here
def matches(timestamp: Int): Boolean = {
// This is not efficient, a better data structure than a List
// should be used, but this is just an example
ranges.contains(_.contains(timestamp))
}
val data: RDD[(Int, T)] = ??? // load the data in an RDD
val filtered = data.filter(x => matches(x.first))
You can do the same with DataFrame/DataSet and UDFs.
This works well if the set of ranges is provided in the driver. If instead it comes from a table, like the 100G data, first collect it back in the driver, if not too big.
Your Spark job goes through 100GB dataset to select relevant data.
I don’t think there is big difference between using SQL or data frame api, as under the hood the full scan happening anyway.
I would consider re-structuring your data, so it is optimised for specific queries.
In your cases partitioning by time can give quite significant improvement (for ex. HIVE table with partitioning).
If you perform search using the same field, that has been used for partitioning - Spark job will only look into relevant partitions.
Related
I am trying multiple ways to optimize executions of large datasets using partitioning. In particular I'm using a function commonly used with traditional SQL databases called nTile.
The objective is to place a certain number of rows into a bucket using a combination of buckettind and repartitioning. This allows Apache Spark to process data more efficient when processing partitioned datasets or should I say bucketted datasets.
Below is two examples. The first example shows how I've used ntile to split a dataset into two buckets followed by repartitioning the data into 2 partitions on the bucketted nTile called skew_data.
I then follow with the same query but without any bucketing or repartitioning.
The problem is query without the bucketting is faster then the query with bucketting, even the query without bucketting places all the data into one partition whereas the query with bucketting splits the query into 2 partitions.
Can someone let me know why that is.
FYI
I'm running the query on a Apache Spark cluster from Databricks.
The cluster just has one single node with 2 cores and 15Gb memory.
First example with nTile/Bucketting and repartitioning
allin = spark.sql("""
SELECT
t1.make
, t2.model
, NTILE(2) OVER (ORDER BY t2.sale_price) AS skew_data
FROM
t1 INNER JOIN t2
ON t1.engine_size = t2.engine_size2
""")
.repartition(2, col("skew_data"), rand())
.drop('skew_data')
The above code splits the data into partitions as follows, with the corresponding partition distribution
Number of partitions: 2
Partitioning distribution: [5556767, 5556797]
The second example: with no nTile/Bucketting or repartitioning
allin_NO_nTile = spark.sql("""
SELECT
t1.make
,t2.model
FROM
t1 INNER JOIN t2
ON t1.engine_size = t2.engine_size2
""")
The above code puts all the data into a single partition as shown below:
Number of partitions: 1
Partitioning distribution: [11113564]
My question is, why is it that the second query(without nTile or repartitioning) is faster than query with nTile and repartitioning?
I have gone to great lengths to write this question out as fully as possible, but if you need further explanation please don't hesitate to ask. I really want to get to the bottom of this.
I abandoned my original approached and used the new PySpark function called bucketBy(). If you want to know how to apply bucketBy() to bucket data go to
https://www.youtube.com/watch?v=dv7IIYuQOXI&list=PLOmMQN2IKdjvowfXo_7hnFJHjcE3JOKwu&index=39
Problem outline: Say I have 300+ GB of data being processed with spark on an EMR cluster in AWS. This data has three attributes used to partition on the filesystem for use in Hive: date, hour, and (let's say) anotherAttr. I want to write this data to a fs in such a way that minimizes the number of files written.
What I'm doing right now is getting the distinct combinations of date, hour, anotherAttr, and a count of how many rows make up combination. I collect them into a List on the driver, and iterate over the list, building a new DataFrame for each combination, repartitioning that DataFrame using the number of rows to guestimate file size, and writing the files to disk with DataFrameWriter, .orc finishing it off.
We aren't using Parquet for organizational reasons.
This method works reasonably well, and solves the problem that downstream teams using Hive instead of Spark don't see performance issues resulting from a high number of files. For example, if I take the whole 300 GB DataFrame, do a repartition with 1000 partitions (in spark) and the relevant columns, and dumped it to disk, it all dumps in parallel, and finishes in ~9 min with the whole thing. But that gets up to 1000 files for the larger partitions, and that destroys Hive performance. Or it destroys some kind of performance, honestly not 100% sure what. I've just been asked to keep the file count as low as possible. With the method I'm using, I can keep the files to whatever size I want (relatively close anyway), but there is no parallelism and it takes ~45 min to run, mostly waiting on file writes.
It seems to me that since there's a 1-to-1 relationship between some source row and some destination row, and that since I can organize the data into non-overlapping "folders" (partitions for Hive), I should be able to organize my code/DataFrames in such a way that I can ask spark to write all the destination files in parallel. Does anyone have suggestions for how to attack this?
Things I've tested that did not work:
Using a scala parallel collection to kick off the writes. Whatever spark was doing with the DataFrames, it didn't separate out the tasks very well and some machines were getting massive garbage collection problems.
DataFrame.map - I tried to map across a DataFrame of the unique combinations, and kickoff writes from inside there, but there's no access to the DataFrame of the data that I actually need from within that map - the DataFrame reference is null on the executor.
DataFrame.mapPartitions - a non-starter, couldn't come up with any ideas for doing what I want from inside mapPartitions
The word 'partition' is also not especially helpful here because it refers both to the concept of spark splitting up the data by some criteria, and to the way that the data will be organized on disk for Hive. I think I was pretty clear in the usages above. So if I'm imagining a perfect solution to this problem, it's that I can create one DataFrame that has 1000 partitions based on the three attributes for fast querying, then from that create another collection of DataFrames, each one having exactly one unique combination of those attributes, repartitioned (in spark, but for Hive) with the number of partitions appropriate to the size of the data it contains. Most of the DataFrames will have 1 partition, a few will have up to 10. The files should be ~3 GB, and our EMR cluster has more RAM than that for each executor, so we shouldn't see a performance hit from these "large" partitions.
Once that list of DataFrames is created and each one is repartitioned, I could ask spark to write them all to disk in parallel.
Is something like this possible in spark?
One thing I'm conceptually unclear on: say I have
val x = spark.sql("select * from source")
and
val y = x.where(s"date=$date and hour=$hour and anotherAttr=$anotherAttr")
and
val z = x.where(s"date=$date and hour=$hour and anotherAttr=$anotherAttr2")
To what extent is y is a different DataFrame than z? If I repartition y, what effect does the shuffle have on z, and on x for that matter?
We had the same problem (almost) and we ended up by working directly with RDD (instead of DataFrames) and implementing our own partitioning mechanism (by extending org.apache.spark.Partitioner)
Details: we are reading JSON messages from Kafka. The JSON should be grouped by customerid/date/more fields and written in Hadoop using Parquet format, without creating too many small files.
The steps are (simplified version):
a)Read the messages from Kafka and transform them to a structure of RDD[(GroupBy, Message)]. GroupBy is a case class containing all the fields that are used for grouping.
b)Use a reduceByKeyLocally transformation and obtain a map of metrics (no of messages/messages size/etc) for each group - eg Map[GroupBy, GroupByMetrics]
c)Create a GroupPartitioner that's using the previously collected metrics (and some input parameters like the desired Parquet size etc) to compute how many partitions should be created for each GroupBy object. Basically we are extending org.apache.spark.Partitioner and overriding numPartitions and getPartition(key: Any)
d)we partition the RDD from a) using the previously defined partitioner: newPartitionedRdd = rdd.partitionBy(ourCustomGroupByPartitioner)
e)Invoke spark.sparkContext.runJob with two parameters: the first one is the RDD partitioned at d), the second one is a custom function (func: (TaskContext, Iterator[T]) that will write the messages taken from Iterator[T] into Hadoop/Parquet
Let's say that we have 100 mil messages, grouped like that
Group1 - 2 mil
Group2 - 80 mil
Group3 - 18 mil
and we decided that we have to use 1.5 mil messages per partition to obtain Parquet files greater than 500MB. We'll end up with 2 partitions for Group1, 54 for Group2, 12 for Group3.
This statement:
I collect them into a List on the driver, and iterate over the list,
building a new DataFrame for each combination, repartitioning that
DataFrame using the number of rows to guestimate file size, and
writing the files to disk with DataFrameWriter, .orc finishing it off.
is completely off-beam where Spark is concerned. Collecting to driver is never a good approach, volumes and OOM issues and latency in your approach is high.
Use so the below so as to simplify and get parallelism of Spark benefits saving time and money for your boss:
df.repartition(cols...)...write.partitionBy(cols...)...
shuffle occurs via repartition, no shuffling ever with partitionBy.
That simple, with Spark's default parallelism utilized.
I'm trying to learn the whole open source big data stack, and I've started with HDFS, Hadoop MapReduce and Spark. I'm more or less limited with MapReduce and Spark (SQL?) for "ETL", HDFS for storage, and no other limitation for other things.
I have a situation like this:
My Data Sources
Data Source 1 (DS1): Lots of data - totaling to around 1TB. I have IDs (let's call them ID1) inside each row - used as a key. Format: 1000s of JSON files.
Data Source 2 (DS2): Additional "metadata" for data source 1. I have IDs (let's call them ID2) inside each row - used as a key. Format: Single TXT file
Data Source 3 (DS3): Mapping between Data Source 1 and 2. Only pairs of ID1, ID2 in CSV files.
My workspace
I currently have a VM with enough data space, about 128GB of RAM and 16 CPUs to handle my problem (the whole project is a research for, not a production-use-thing). I have CentOS 7 and Cloudera 6.x installed. Currently, I'm using HDFS, MapReduce and Spark.
The task
I need only some attributes (ID and a few strings) from Data Source 1. My guess is that it comes to less than 10% in data size.
I need to connect ID1s from DS3 (pairs: ID1, ID2) to IDs in DS1 and ID2s from DS3 (pairs: ID1, ID2) to IDs in DS2.
I need to add attributes from DS2 (using "mapping" from the previous bullet) to my extracted attributes from DS1
I need to make some "queries", like:
Find the most used words by years
Find the most common words, used by a certain author
Find the most common words, used by a certain author, on a yearly basi
etc.
I need to visualize data (i.e. wordclouds, histograms, etc.) at the end.
My questions:
Which tool to use to extract data from JSON files the most efficient way? MapReduce or Spark (SQL?)?
I have arrays inside JSON. I know the explode function in Spark can transpose my data. But what is the best way to go here? Is it the best way to
extract IDs from DS1 and put exploded data next to them, and write them to new files? Or is it better to combine everything? How to achieve this - Hadoop, Spark?
My current idea was to create something like this:
Extract attributes needed (except arrays) from DS1 with Spark and write them to CSV files.
Extract attributes needed (exploded arrays only + IDs) from DS1 with Spark and write them to CSV files - each exploded attribute to own file(s).
This means I have extracted all the data I need, and I can easily connect them with only one ID. I then wanted to make queries for specific questions and run MapReduce jobs.
The question: Is this a good idea? If not, what can I do better? Should I insert data into a database? If yes, which one?
Thanks in advance!
Thanks for asking!! Being a BigData developer for last 1.5 years and having experience with both MR and Spark, I think I may guide you to the correct direction.
The final goals which you want to achieve can be obtained using both MapReduce and Spark. For visualization purpose you can use Apache Zeppelin, which can run on top of your final data.
Spark jobs are memory expensive jobs, i.e, the whole computation for spark jobs run on memory, i.e, RAM. Only the final result is written to the HDFS. On the other hand, MapReduce uses less amount of memory and used HDFS for writing intermittent stage results, thus making more I/O operations and more time consuming.
You can use Spark's Dataframe feature. You can directly load data to Dataframe from a structured data (it can be plaintext file also) which will help you to get the required data in a tabular format. You can write the Dataframe to a plaintext file, or you can store to a hive table from where you can visualize data. On the other hand, using MapReduce you will have to first store in Hive table, then write hive operations to manipulate data, and store final data to another hive table. Writing native MapReduce jobs can be very hectic so I would suggest to refrain from choosing that option.
At the end, I would suggest to use Spark as processing engine (128GB and 16 cores is enough for spark) to get your final result as soon as possible.
We are using Cassandra DataStax 6.0 and Spark enabled. We have 10GB of data coming every day. All queries are based on date. We have one huge table with 40 columns. We are planning to generate reports using Spark. What is the best way to setup this data. Since we keep getting data every day and save data for around 1 year in one table.
We tried to use different partition but most of our keys are based on date.
No code just need suggestion
Our query should be fast enough. We have 256GB Ram with 9 nodes. 44 core CPU.
Having the data organized in the daily partitions isn't very good design - in this case, only RF nodes will be active during the day writing the data, and then at the time of the report generation.
Because you'll be accessing that data only from Spark, you can use following approach - have some bucket field as partition key, for example, with uniformly generated random number, and timestamp as a clustering column, and maybe another uuid column for uniqueness guarantee of records, something like this:
create table test.sdtest (
b int,
ts timestamp,
uid uuid,
v1 int,
primary key(b, ts, uid));
Where maximum value for generatio of b should be selected to have not too very big and not very small partitions, so we can effectively read them.
And then we can run Spark code like this:
import org.apache.spark.sql.cassandra._
val data = spark.read.cassandraFormat("sdtest", "test").load()
val filtered = data.filter("ts >= cast('2019-03-10T00:00:00+0000' as timestamp) AND ts < cast('2019-03-11T00:00:00+0000' as timestamp)")
The trick here is that we distribute data across the nodes by using the random partition key, so the all nodes will handle the load during writing the data and during the report generation.
If we look into physical plan for that Spark code (formatted for readability):
== Physical Plan ==
*Scan org.apache.spark.sql.cassandra.CassandraSourceRelation [b#23,ts#24,v1#25]
PushedFilters: [*GreaterThanOrEqual(ts,2019-03-10 00:00:00.0),
*LessThan(ts,2019-03-11 00:00:00.0)], ReadSchema: struct<b:int,ts:timestamp,v1:int>
We can see that both conditions will be pushed to DSE on the CQL level - this means, that Spark won't load all data into memory and filter them, but instead all filtering will happen in Cassandra, and only necessary data will be returned back. And because we're spreading requests between multiple nodes, the reading could be faster (need to test) than reading one giant partition. Another benefit of this design, is that it will be easy to perform deletion of the old data using Spark, with something like this:
val toDel = sc.cassandraTable("test", "sdtest").where("ts < '2019-08-10T00:00:00+0000'")
toDel.deleteFromCassandra("test", "sdtest", keyColumns = SomeColumns("b", "ts"))
In this case, Spark will perform very effective range/row deletion that will generate less tombstones.
P.S. it's recommended to use DSE's version of the Spark connector as it may have more optimizations.
P.P.S. theoretically, we can merge ts and uid into one timeuuid column, but I'm not sure that it will work with Dataframes.
I have a problem of how to use spark to manipulate/iterate/scan multiple tables of cassandra. Our project uses spark&spark-cassandra-connector connecting to cassandra to scan multiple tables , try to match related value in different tables and if matched, take the extra action such as table inserting. The use case is like below:
sc.cassandraTable(KEYSPACE, "table1").foreach(
row => {
val company_url = row.getString("company_url")
sc.cassandraTable(keyspace, "table2").foreach(
val url = row.getString("url")
val value = row.getString("value")
if (company_url == url) {
sc.saveToCassandra(KEYSPACE, "target", SomeColumns(url, value))
}
)
})
The problems are
As spark RDD is not serializable, the nested search will fail cause sc.cassandraTable returns a RDD. The only way I know to work around is to use sc.broadcast(sometable.collect()). But if the sometable is huge, the collect will consume all the memory. And also, if in the use case, several tables use the broadcast, it will drain the memory.
Instead of broadcast, can RDD.persist handle the case? In my case, I use sc.cassandraTable to read all tables in RDD and persist back to disk, then retrieve the data back for processing. If it works, how can I guarantee the rdd read is done by chunks?
Other than spark, is there any other tool (like hadoop etc.??) which can handle the case gracefully?
It looks like you are actually trying to do a series of Inner Joins. See the
joinWithCassandraTable Method
This allows you to use the elements of One RDD to do a direct query on a Cassandra Table. Depending on the fraction of data you are reading from Cassandra this may be your best bet. If the fraction is too large though you are better off reading the two table separately and then using the RDD.join method to line up rows.
If all else fails you can always manually use the CassandraConnector Object to directly access the Java Driver and do raw requests with that from a distributed context.