I'm using the WriteKafaP vertex in a Jet job. Is there a way to plug in custom partition logic when publishing to the Kafka topic?
In 0.4, you can use a custom Kafka partitioner configuration. In 0.5, we added the ability to map the item to a ProducerRecord directly.
For example:
Vertex v = new Vertex("kafkaSink", KafkaProcessors.writeKafkaP(properties, o -> new ProducerRecord(topic, partition, key, value));
Related
I am using Spark streaming and data is being sent to Kafka. I am sending a Map to Kafka. Assuming I have a Map of 20(which may grow to 1000 in a Streaming Batch duration) elements like below:
HashMap<Integer,String> input = new HashMap<Integer,String>();
input.put(11,"One");
input.put(312,"two");
input.put(33,"One");
input.put(24,"One");
input.put(35,"One");
input.put(612,"One");
input.put(7,"One");
input.put(128,"One");
input.put(9,"One");
input.put(10,"One");
input.put(11,"One1");
input.put(12,"two1");
input.put(13,"One1");
input.put(14,"One1");
input.put(15,"One1");
input.put(136,"One1");
input.put(137,"One1");
input.put(158,"One1");
input.put(159,"One1");
input.put(120,"One1");
Set<Integer> inputKeys = input.keySet();
Iterator<Integer> inputKeysIterator = inputKeys.iterator();
while (inputKeysIterator.hasNext()) {
Integer key = inputKeysIterator.next();
ProducerRecord<Integer, String> record = new ProducerRecord<Integer, String>(topic,
key%10, input.get(key));
KafkaProducer.send(record);
}
My Kafka topic is having 10 partitions. Here I am calling kafkaProducer.send() 20 times and hence 20 Kafka call. how can I send whole data in a batch i.e. in one Kafka call, but again I want to ensure each record goes to specific partition driven by formula key%10 as in
ProducerRecord record = new ProducerRecord(topic,
key%10, input.get(key));
Options I see: linger.ms=1 may ensure that but with a latency of 1ms.
How to avoid this latency and to avoid 20 network(Kafka) call or to minimize Kafka calls?
The Kafka Producer API already sends messages in batches even though you call individually one by one
See batch.size in the docs, it is by bytes, not messages, but you can force an actual network event by calling flush on the Producer
Regarding the Partitions, you'll need to create your code Partitioner. Simply passing the mod value as a key doesn't guarantee you won't have a hash collision in the default partitioner
import org.apache.hadoop.hbase.mapreduce.TableOutputFormat
import org.apache.hadoop.hbase.mapreduce.TableInputFormat
import org.apache.hadoop.mapreduce.Job
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.spark.rdd.PairRDDFunctions
def bulkWriteToHBase(sparkSession: SparkSession, sparkContext: SparkContext, jobContext: Map[String, String], sinkTableName: String, outRDD: RDD[(ImmutableBytesWritable, Put)]): Unit = {
val hConf = HBaseConfiguration.create()
hConf.set("hbase.zookeeper.quorum", jobContext("hbase.zookeeper.quorum"))
hConf.set("zookeeper.znode.parent", jobContext("zookeeper.znode.parent"))
hConf.set(TableInputFormat.INPUT_TABLE, sinkTableName)
val hJob = Job.getInstance(hConf)
hJob.getConfiguration().set(TableOutputFormat.OUTPUT_TABLE, sinkTableName)
hJob.setOutputFormatClass(classOf[TableOutputFormat[ImmutableBytesWritable]])
outRDD.saveAsNewAPIHadoopDataset(hJob.getConfiguration())
}
what I have found by using this hbase bulk insertion is that, every time spark will only write into one single region server from hbase, which becomes the bottleneck.
however when I use almost the same approach but reading from hbase, it is using multiple executors to do parallel reading .
def bulkReadFromHBase(sparkSession: SparkSession, sparkContext: SparkContext, jobContext: Map[String, String], sourceTableName: String) = {
val hConf = HBaseConfiguration.create()
hConf.set("hbase.zookeeper.quorum", jobContext("hbase.zookeeper.quorum"))
hConf.set("zookeeper.znode.parent", jobContext("zookeeper.znode.parent"))
hConf.set(TableInputFormat.INPUT_TABLE, sourceTableName)
val inputRDD = sparkContext.newAPIHadoopRDD(hConf, classOf[TableInputFormat], classOf[ImmutableBytesWritable], classOf[Result])
inputRDD
}
can anyone please explain why this could happen? or maybe I have
used the wrong way for spark-hbase bulk I/O ?
Question : I have used the wrong way for spark-hbase bulk I/O ?
No your way is right, although, you need to pre-split regions before hand & create table with presplit regions.
for example create 'test_table', 'f1', SPLITS=> ['1', '2', '3', '4', '5', '6', '7', '8', '9']
Above table occupies 9 regions..
design good rowkey with will starts with 1-9
you can use guava murmur hash like below.
import com.google.common.hash.HashCode;
import com.google.common.hash.HashFunction;
import com.google.common.hash.Hashing;
/**
* getMurmurHash.
*
* #param content
* #return HashCode
*/
public static HashCode getMurmurHash(String content) {
final HashFunction hf = Hashing.murmur3_128();
final HashCode hc = hf.newHasher().putString(content, Charsets.UTF_8).hash();
return hc;
}
final long hash = getMurmur128Hash(Bytes.toString(yourrowkey as string)).asLong();
final int prefix = Math.abs((int) hash % 9);
now append this prefix to your rowkey
For example
1rowkey1 // will go in to first region
2rowkey2 // will go in to
second region
3rowkey3 // will go in to third region
...
9rowkey9 //
will go in to ninth region
If you are doing pre-splitting, and want to manually manage region splits, you can also disable region splits, by setting hbase.hregion.max.filesize to a high number and setting the split policy to ConstantSizeRegionSplitPolicy. However, you should use a safeguard value of like 100GB, so that regions does not grow beyond a region server’s capabilities. You can consider disabling automated splitting and rely on the initial set of regions from pre-splitting for example, if you are using uniform hashes for your key prefixes, and you can ensure that the read/write load to each region as well as its size is uniform across the regions in the table
1) please ensure that you can presplit the table before loading data in to hbase table 2) Design good rowkey as Explained below using murmurhash or some other hashing technique. to ensure uniform distribution across the regions.
Also look at http://hortonworks.com/blog/apache-hbase-region-splitting-and-merging/
Question : can anyone please explain why this could happen?
reason is quite obvious and simple HOT SPOTTING of data in to one specific reason becuase of poor rowkey for that table...
Consider a hashmap in java which has elements with hashcode 1234. then it will fill all the elements in one bucket isntit ? If hashmap elements are distributed across different good hashcode then it will put elements in different buckets. same is the case with hbase. here your hashcode is just like your rowkey...
Further more,
What happens if I already have a table and I want to split the regions
across...
The RegionSplitter class provides several utilities to help in the administration lifecycle for developers who choose to manually split regions instead of having HBase handle that automatically.
The most useful utilities are:
Create a table with a specified number of pre-split regions
Execute a rolling split of all regions on an existing table
Example :
$ hbase org.apache.hadoop.hbase.util.RegionSplitter test_table HexStringSplit -c 10 -f f1
where -c 10, specifies the requested number of regions as 10, and -f specifies the column families you want in the table, separated by “:”. The tool will create a table named “test_table” with 10 regions:
13/01/18 18:49:32 DEBUG hbase.HRegionInfo: Current INFO from scan results = {NAME => 'test_table,,1358563771069.acc1ad1b7962564fc3a43e5907e8db33.', STARTKEY => '', ENDKEY => '19999999', ENCODED => acc1ad1b7962564fc3a43e5907e8db33,}
13/01/18 18:49:32 DEBUG hbase.HRegionInfo: Current INFO from scan results = {NAME => 'test_table,19999999,1358563771096.37ec12df6bd0078f5573565af415c91b.', STARTKEY => '19999999', ENDKEY => '33333332', ENCODED => 37ec12df6bd0078f5573565af415c91b,}
...
as discussed in comment, you found that my final RDD right before writing into hbase only has 1 partition! which indicates that there
was only one executor holding the entire data... I am still trying to
find out why.
Also, Check
spark.default.parallelism defaults to the number of all cores on all
machines. The parallelize api has no parent RDD to determine the
number of partitions, so it uses the spark.default.parallelism.
So You can increase partitions by repartitioning.
NOTE : I observed that, In Mapreduce The number of partitions of the regions/input split = number of mappers launched.. Similarly in your case it may be the same situation where data loaded in to one particular region thats why one executor lauched. please verify that as well
Though you have not provided example data or enough explanation,this is mostly not due to your code or configuration.
It is happening so,due to non-optimal rowkey design.
The data you are writing is having keys(hbase rowkey) improperly structured(maybe monotonically increasing or something else).So, write to one of the regions is happening.You can prevent that thro' various ways(various recommended practices for rowkey design like salting,inverting,and other techniques).
For reference you can see http://hbase.apache.org/book.html#rowkey.design
In case,if you are wondering whether the write is done in parallel for all regions or one by one(not clear from question) look at this :
http://hbase.apache.org/book.html#_bulk_load.
What is the best way to partition the data by a field into predefined partition count?
I am currently partitioning the data by specifying the partionCount=600. The count 600 is found to give best query performance for my dataset/cluster setup.
val rawJson = sqlContext.read.json(filename).coalesce(600)
rawJson.write.parquet(filenameParquet)
Now I want to partition this data by the column 'eventName' but still keep the count 600. The data currently has around 2000 unique eventNames, plus the number of rows in each eventName is not uniform. Around 10 eventNames have more than 50% of the data causing data skew. Hence if I do the partitioning like below, its not very performant. The write is taking 5x more time than without.
val rawJson = sqlContext.read.json(filename)
rawJson.write.partitionBy("eventName").parquet(filenameParquet)
What is a good way to partition the data for these scenarios? Is there a way to partition by eventName but spread this into 600 partitions?
My schema looks like this:
{
"eventName": "name1",
"time": "2016-06-20T11:57:19.4941368-04:00",
"data": {
"type": "EventData",
"dataDetails": {
"name": "detailed1",
"id": "1234",
...
...
}
}
}
Thanks!
This is a common problem with skewed data and there are several approaches you can take.
List bucketing works if the skew remains stable over time, which may or may not be the case, especially if new values of the partitioning variable are introduced. I have not researched how easy it is to adjust list bucketing over time and, as your comment states, you can't use that anyway because it is a Spark 2.0 feature.
If you are on 1.6.x, the key observation is that you can create your own function that maps each event name into one of 600 unique values. You can do this as a UDF or as a case expression. Then, you simply create a column using that function and then partition by that column using repartition(600, 'myPartitionCol) as opposed to coalesce(600).
Because we deal with very skewed data at Swoop, I've found the following workhorse data structure to be quite useful for building partitioning-related tools.
/** Given a key, returns a random number in the range [x, y) where
* x and y are the numbers in the tuple associated with a key.
*/
class RandomRangeMap[A](private val m: Map[A, (Int, Int)]) extends Serializable {
private val r = new java.util.Random() // Scala Random is not serializable in 2.10
def apply(key: A): Int = {
val (start, end) = m(key)
start + r.nextInt(end - start)
}
override def toString = s"RandomRangeMap($r, $m)"
}
For example, here is how we build a partitioner for a slightly different case: one where the data is skewed and the number of keys is small so we have to increase the number of partitions for the skewed keys while sticking with 1 as the minimum number of partitions per key:
/** Partitions data such that each unique key ends in P(key) partitions.
* Must be instantiated with a sequence of unique keys and their Ps.
* Partition sizes can be highly-skewed by the data, which is where the
* multiples come in.
*
* #param keyMap maps key values to their partition multiples
*/
class ByKeyPartitionerWithMultiples(val keyMap: Map[Any, Int]) extends Partitioner {
private val rrm = new RandomRangeMap(
keyMap.keys
.zip(
keyMap.values
.scanLeft(0)(_+_)
.zip(keyMap.values)
.map {
case (start, count) => (start, start + count)
}
)
.toMap
)
override val numPartitions =
keyMap.values.sum
override def getPartition(key: Any): Int =
rrm(key)
}
object ByKeyPartitionerWithMultiples {
/** Builds a UDF with a ByKeyPartitionerWithMultiples in a closure.
*
* #param keyMap maps key values to their partition multiples
*/
def udf(keyMap: Map[String, Int]) = {
val partitioner = new ByKeyPartitionerWithMultiples(keyMap.asInstanceOf[Map[Any, Int]])
(key:String) => partitioner.getPartition(key)
}
}
In your case, you have to merge several event names into a single partition, which would require changes but I hope the code above gives you an idea how to approach the problem.
One final observation is that if the distribution of event names values a lot in your data over time, you can perform a statistics gathering pass over some part of the data to compute a mapping table. You don't have to do this all the time, just when it is needed. To determine that, you can look at the number of rows and/or size of output files in each partition. In other words, the entire process can be automated as part of your Spark jobs.
I am trying to optimize my spark job by avoiding shuffling as much as possible.
I am using cassandraTable to create the RDD.
The column family's column names are dynamic, thus it is defined as follows:
CREATE TABLE "Profile" (
key text,
column1 text,
value blob,
PRIMARY KEY (key, column1)
) WITH COMPACT STORAGE AND
bloom_filter_fp_chance=0.010000 AND
caching='ALL' AND
...
This definition results in CassandraRow RDD elements in the following format:
CassandraRow <key, column1, value>
key - the RowKey
column1 - the value of column1 is the name of the dynamic column
value - the value of the dynamic column
So if I have RK='profile1', with columns name='George' and age='34', the resulting RDD will be:
CassandraRow<key=profile1, column1=name, value=George>
CassandraRow<key=profile1, column1=age, value=34>
Then I need to group elements that share the same key together to get a PairRdd:
PairRdd<String, Iterable<CassandraRow>>
Important to say, that all the elements I need to group are in the same Cassandra node (share the same row key), so I expect the connector to keep the locality of the data.
The problem is that using groupBy or groupByKey causes shuffling. I rather group them locally, because all the data is on the same node:
JavaPairRDD<String, Iterable<CassandraRow>> rdd = javaFunctions(context)
.cassandraTable(ks, "Profile")
.groupBy(new Function<ColumnFamilyModel, String>() {
#Override
public String call(ColumnFamilyModel arg0) throws Exception {
return arg0.getKey();
}
})
My questions are:
Does using keyBy on the RDD will cause shuffling, or will it keep the data locally?
Is there a way to group the elements by key without shuffling? I read about mapPartitions, but didn't quite understand the usage of it.
Thanks,
Shai
I think you are looking for spanByKey, a cassandra-connector specific operation that takes advantage of the ordering provided by cassandra to allow grouping of elements without incurring in a shuffle stage.
In your case, it should look like:
sc.cassandraTable("keyspace", "Profile")
.keyBy(row => (row.getString("key")))
.spanByKey
Read more in the docs:
https://github.com/datastax/spark-cassandra-connector/blob/master/doc/3_selection.md#grouping-rows-by-partition-key
I'm developing a mechanism for Cassandra using Hector.
What I need at this moment is to know which are the hash values of the keys to look at which node is stored (looking at the tokens of each one), and ask directly this node for the value. What I understood is that depending on the partitioner Cassandra uses, the values are stored independently from one partitioner to other. So, are the hash values of all keys stored in any table? In case not, how could I implement a generic class that once I read from System Keyspace the partitioner that is using Cassandra this class could be an instance of it without the necessity of modifying the code depending on the partitioner? I would need it to call the getToken method to calculate the hash value for a given key.
Hector's CqlQuery is poorly supported and buggy. You should use the native Java CQL driver instead: https://github.com/datastax/java-driver
You could just reuse the partitioners defined in Cassandra: https://github.com/apache/cassandra/tree/trunk/src/java/org/apache/cassandra/dht and then using the token ranges you could do the routing.
The CQL driver offers token-aware routing out of the box. I would use that instead of trying to reinvent the wheel in Hector, especially since Hector uses the legacy Thrift API instead of CQL.
Finally after testing different implementations I found the way to get the partitioner using the next code:
CqlQuery<String, String, String> cqlQuery = new CqlQuery<String, String, String>(
ksp, StringSerializer.get(), StringSerializer.get(), StringSerializer.get());
cqlQuery.setQuery("select partitioner from local");
QueryResult<CqlRows<String, String, String>> result = cqlQuery.execute();
CqlRows rows = result.get();
for (int i = 0; i < rows.getCount(); i++) {
RowImpl<String, String, String> row = (RowImpl<String, String, String>) rows
.getList().get(i);
List<HColumn<String, String>> column = row.getColumnSlice().getColumns();
for (HColumn<String , String> c: column) {
System.out.println(c.getValue());
}
}