I'm writing a graph-related program in Scala with Spark. The dataset have 4 million nodes and 4 million edges(you can treat this as a tree), but for each time(an Iteration), I only edit a portion of it, namely a sub-tree rooted by a given node, and the nodes in a path between that given node and root.
The Iteration has dependency, which means i+1 Iteration needs the result coming from i. So I need store the result of each Iteration for next step.
I'm trying to find an efficient way to update RDD, but have no clue so far.I find that PairRDD have a lookup function which could reduce the computation time from O(N), to O(M), N denote the total number of objects in RDD and M denote the number of elements in each partition.
So I'm thinking is there anyway that I could update an object in the RDD with O(M)? Or more ideally, O(1)?(I see an email in Spark's mail list saying that the lookup can be modified to achieve O(1))
Another thing is, if I could achieve O(M) for updating the RDD, could I increase the partition to some number larger than the number of cores I have and achieve a better performance?
As functional data structures, RDDs are immutable and an operation on an RDD generates a new RDD.
Immutability of the structure does not necessarily mean full replication. Persistant data structures are a common functional pattern where operations on immutable structures yield a new structure but previous versions are maintained and often reused.
GraphX (a 'module' on top of Spark) is a graph API on top of Spark that uses such concept: From the docs:
Changes to the values or structure of the graph are accomplished by
producing a new graph with the desired changes. Note that substantial
parts of the original graph (i.e., unaffected structure, attributes,
and indicies) are reused in the new graph reducing the cost of this
inherently functional data-structure.
It might be a solution for the problem at hand: http://spark.apache.org/docs/1.0.0/graphx-programming-guide.html
An RDD is a distributed data set, a partition is the unit for RDD storage, and the unit to process and RDD is an element.
For example, you read a large file from HDFS as an RDD, then the element of this RDD is String(lines in that file), and spark stores this RDD across the cluster by partition. For you, as a spark user, you only need to care about how to deal with the lines of that files, just like you are writing a normal program, and you read a file from local file system line by line. That's the power of spark:)
Anyway, you have no idea which elements will be stored in a certain partition, so it doesn't make sense to update a certain partition.
The MapReduce programming model (and FP) doesn't really support updates of single values. Rather one is supposed to define a sequence of transformations.
Now when you have interdependent values, i.e. you cannot perform your transformation with a simple map but need to aggregate multiple values and update based on that value, then what you need to do is think of a way of grouping those values together then transforming each group - or define a monoidal operation so that the operation can be distributed and chopped up into substeps.
Group By Approach
Now I'll try to be a little more specific for your particular case. You say you have subtrees, is it possible to first map each node to an key that indicates the corresponding subtree? If so you could do something like this:
nodes.map(n => (getSubTreeKey(n), n)).grouByKey().map ...
Monoid
(strictly speaking you want a commutative monoid) Best you read http://en.wikipedia.org/wiki/Monoid#Commutative_monoid
For example + is a monoidal operation because when one wishes to compute the sum of, say, an RDD of Ints then the underlying framework can chop up the data into chunks, perform the sum on each chunk, then sum up the resulting sums (possibly in more than just 2 steps too). If you can find a monoid that will ultimately produce the same results you require from single updates, then you have a way to distribute your processing. E.g.
nodes.reduce(_ myMonoid _)
Related
I am new to Spark and Scala. I was reading upon distinct() function of Spark. But I could not find any proper details . I have a few doubts which I could not resolve and have written them down .
How distinct() is implemented in Spark ?
I am not that good with Spark source code to be able to identify the whole flow .
When I check for execution plan, I can only see a ShuffleRDD
What is the Time Complexity of distinct ?
I also found from Google searching that it also uses hashing and sorting in some way .
So, I thought whether it uses the same principle as getting unique elements from array with help of Hashset .
If it was one system , I would have guessed that time complexity is O(nlogn) .
But it is distributed among many partitions and shuffled , what would be order of time complexity ?
Is there a way to avoid shuffling in particular cases ?
If I make sure to properly partition my data as per my use-case ,
can I avoid shuffling ?
i.e. for example , say exploding an ArrayType column in dataframe with unique rows creates new rows with other columns being duplicated .
I will select the other columns .
In this way I made sure duplicates are unique per partition .
Since I know duplicates are unique per partition ,
I can avoid shuffle and just keenly drop duplicates in that partition
I also found this Does spark's distinct() function shuffle only the distinct tuples from each partition .
Thanks For your help .
Please correct me if I am wrong anywhere .
How distinct() is implemented in Spark ?
By applying a dummy aggregation with None value. Roughly
rdd.map((_, None)).reduceByKey((a, b) => a)
What is the Time Complexity of distinct ?
Given overall complexity of the process it is hard to estimate. It is at least O(N log N), as shuffle requires sort, but given multiple other operations required to build additional off core data structures (including associative arrays), serialize / deserialize the data can be higher, and in practice dominated by IO operations, not pure algorithm complexity.
Is there a way to avoid shuffling in particular cases ?
Yes, if potential duplicates are guaranteed to be placed on the same partition.,
You can use mapPartitions to dedpulicate the data, especially if data is sorted or in other way guaranteed to have duplicates in a isolated neighborhood. Without this you might be limited by the memory requirements, unless you accept approximate results with probabilistic filter (like Bloom filter).
In general though it is not possible, and operation like this will be non-local.
One
First I read a tweets and parse into a tweet case class through a map into my parsing function parseTweet:
val tweets = sc.textFile("/home/gakuo/Documents/bigdata/NintendoTweets").map(parseTweet)
Two
Then I use a function to pair RDD that results into a pair RDD of the form (hashtags, likes) through a map inside toPairRdd:
val pairedRDD = toPairRdd(tweets).persist()
Question
After reading in my RDD in (one) above, does it help to persist it as what follows in (two)is a transformation? I am thinking, since both as lazy, then persisting is actually a waste of memory.
Three
After computing the pairRDD, I want to compute scores of each hashtag:toScores uses reduceByKey
val scores = toScores(pairedRDD).persist()
Question
I use reduceByKey. Does this pairRDD method result in shuffling? I have read a paper that states:
"a shuffle can occur when the resulting RDD depends on other elements from the same RDD or another RDD.
cogroup, groupWith, join, leftOuterJoin, rightOuterJoin,
groupByKey, reduceByKey, combineByKey, distinct, intersection,
repartition, coalesce resulting in shuffling. To avoid shuffles for these kinds of operations make sure the transformation follows the same partition as the original RDD"
The same paper also states that reduceByKey follows the same partition as the original RDD.
It's matter to use persist ( on mem/ disk/both) when you have many actions which always do the number of the same transformations again. And if it takes too long to recompute again & again.
In your case there is no persist or caching required as it is a one-pass process. You need to know that Stages are genned putting as many transformations together before shuffling. You would have 2 here.
If you were to process some other data requirements using the pairedRDD, then persist would be advisable.
The actions are more relevant in any event.
If you have multiple actions using the same rdd then it’s advisable to persist. I don’t see any action till now in your code. So I don’t see any reason to cache the rdd. Persist/cache also lazily evaluated.
Persist/cache - it is not guaranteed that data will be stayed during life time of execution as persisting follows LRU least recently used algorithm which may flush the data on basis of least used rdd if the memory is full. All the things need to keep in mind while using persist.
Reducebykey - it’s a wide transformation as shuffle may happen. But first of all it does combine the data w.r.t a key inside the partition first then do a reduce operation after that. So it’s less costly. Always avoid groupbykey where it shuffles the data directly without combining the the data w.r.t a key in a partition. Please avoid groupbykey while coding.
So I have a RDD, this RDD is paired together with indexes of its elements. I would like to simply iterate over it (or maybe if there is a nice Spark function to do this) and check adjacent elements by comparing one of their values. If the adjacent elements fulfill this check, I would like to note down their indexes in a different non RDD structure, maybe a ListBuffer.
Is this possible to do with some kind of Spark special function or do I have to simply iteratue through it manually, and how would I then iterate through it?
One of the main characteristics of an RDD is that it is immutable. Once it is created, you can iterate over it as many times as you want, but you won't be able to make any changes to it.
If you want to make changes, you need to create a new RDD via a transformation.
Additionally, if you want to iterate over an RDD and check adjacent elements, this logic will most likely not work very well as an RDD is distributed and you will usually not be able to have any guarantee of which records are next to each other. You could control by specifying a partitioner to group your data, but still I wouldn't count on it unless you explicitly use a function to group your data.
If you post some sample data it may be easier to help you with your question.
Like an example below, I'd like to accumulate values by key.
I can use List, ArrayBuffer, Array, mutable.HashSet, etc.
When the number of values for each key is large varied and unknown number, i.e wide (e.g, 10k - 1M), which data structure is most efficient?
Definitely, in Java, I avoid to use List or Vector due to memory dynamic expansion. In Scala, performance-wise and/or memory-wise what is best practice?
Thanks.
val res = data.flatMap{ x =>
if ( some condition )
Some(( x._2._2, ArrayBuffer[(Int, Double)]( x._1,, x._2._1)) ) )
} else {
None
}
}
.reduceByKey {(x, y) => x ++ y}
UPDATE:
The subsequent transforms are as below on Spark. I'm creating feature matrix (using sparse vector) as data prep.
.map(x => (x._1, x._2.toArray.sortBy(_._1 )) )
.map { x => (yieldMap.value.get(x._1).get , x._2.map(_._1), x._2.map(_._2)) }
Well, if you accumulate them for quick access, then of course you need something that provides O(1) lookup (such as HashMap). From your example I can see that you want to reduce by key in a later stage, which means you need to traverse it anyway.
List is OK if you need to append only to head of the collection. In that case make a ListBuffer, fill it up incrementally and then invoke .toList() when you're done adding. That will save you some memory.
If you don't append only to head, take a Vector. It is effectively constant time due to its tree representation (see here) and is generally recommended over lists if performance is an issue.
Here's a performance overview that might be of help.
You seem to be using spark, so I assume you want to compute this stuff on a cluster somehow? When doing distributed computing, the question how you distribute and how much communication is needed between cluster nodes is the most important.
The fastest approach probably would be to map each key to a cluster node and then aggregate the results sequentially into a list. From looking at the API you can achieve the mapping to cluster nodes using a Partitioner and the aggregation using aggregateByKey. AggregateByKey allows you to specify a function that is applied in linear order over the data in on partition, so you can aggregate all values effectively into a list. You also have to specify a associative aggregate function but it does not matter how efficient it is because it will never be called.
If you stick with what you have, without knowing of being able to assume anything of the order in which the reduce function is called, a plain Array might actually be the best data structure. Lists might be faster if you are prepending elements, but you cannot ensure that. Vectors on the other hand have effectively constant time for appending and prepending an element, but merging of two vectors of similar size should be linear anyway, and the constants involved with vectors are larger. If you have an efficiency problem with what you are doing now, I would really try to use aggregate together with an optimal partitioning of your data.
MongoDB's explanation of the reduce phase says:
The map/reduce engine may invoke reduce functions iteratively; thus,
these functions must be idempotent.
This is how I always understood reduce to work in a general map reduce environment.
Here you could sum values across N machines by reducing the values on each machine, then sending those outputs to another reducer.
Wikipedia says:
The framework calls the application's Reduce function once for each
unique key in the sorted order. The Reduce can iterate through the
values that are associated with that key and produce zero or more
outputs.
Here you would need to move all values (with the same key) to the same machine to be summed. Moving data to the function seems to be the opposite of what map reduce is supposed to do.
Is Wikipedia's description too specific? Or did MongoDB break map-reduce? (Or am I missing somethieng here?)
This is how the original Map Reduce framework was described by Google:
2 Programming Model
[...]
The intermediate values are supplied to the user’s reduce function via an iterator. This allows us to handle lists of values that are too large to fit in memory.
And later:
3 Implementation
[...]
6. The reduce worker iterates over the sorted intermediate data and for each unique intermediate key encountered, it passes the key and the corresponding set of intermediate values to the user’s Reduce function.
So there is only one invocation of Reduce. The problem of moving a lot of small intermediate pairs is addressed by using special combiner function locally:
4.3 Combiner Function
In some cases, there is significant repetition in the intermediate keys produced by each map task [...] We allow the user to specify an optional Combiner function that does partial merging of this data before it is sent over the network.
The Combiner function is executed on each machine that performs a map task. Typically the same code is used to implement both the combiner and the reduce functions. [...]
Partial combining significantly speeds up certain classes of MapReduce operations.
TL;DR
Wikipedia follows original MapReduce design, MongoDB designers taken a slightly different approach.
According to the Google MapReduce paper
When a reduce worker has read all intermediate data, it sorts it by the intermediate keys
so that all occurrences of the same key are grouped together.
MongoDB document says
The map/reduce engine may invoke reduce functions iteratively; thus, these functions must be idempotent.
So, in case of the MapReduce as defined in the Google paper the reduce starts processing the key/value pairs once the data for a particular key has been transferred to the reducer. But, as Tomasz mentioned MongoDB seems to implement MapReduce in a slightly different way.
In the MapReduce proposed by Google either Map or Reduce tasks will be processing the KV pairs, but in the MongoDB implementation the Map and Reduce tasks will be simultaneously process the KV pairs. The MongoDB approach might not be efficient, since the nodes are not efficiently used and there is a chance that the Map and Reduce slots in the cluster are full and may not run new jobs.
The catch in Hadoop is although the reducers tasks don't process the KV pairs till the maps are done processing the data, the reducers tasks can be spawned before the mappers complete the processing. The parameter "mapreduce.job.reduce.slowstart.completedmaps" and is set to "0.05" and the description says "Fraction of the number of maps in the job which should be complete before reduces are scheduled for the job."
Here you would need to move all values (with the same key) to the same machine to be summed. Moving data to the function seems to be the opposite of what map reduce is supposed to do.
Also, the data locality is considered for the map tasks and not the reduce tasks. For the reduce tasks the data has to be moved from different mappers on different nodes to the reducers for aggregation.
Just my 2c.
TLDR : reduce (mongo) is like the combiner, and finalize(mongo) is almost like the reducer except that it takes just one key/value. If you need to have all your data in the reduce (hadoop) function, aggregate it with the reduce (mongo) into a big array and pass it to finalize. Use some sort of flags in the output values to do so.
That's how I do it and I think it would suck for big loads of data but I don't know of any other way to do it with mongodb mapreduce :( (but Im not very experienced with it)