spark performance tuning interview questions

Here are the list of most frequently asked Spark Interview Questions and Answers in technical interviews. Data locality is how close data is to the code processing it. This type of join broadcasts one side to all executors, and so requires more memory for broadcasts in general. f.collect_set(AM.msg).alias(“agg_src”), Once that timeout expires, it starts moving the data from far away to the free CPU. Oracle Performance Tuning Interview Questions and Answers. This is due to several reasons: To further tune garbage collection, we first need to understand some basic information about memory management in the JVM: Java Heap space is divided in to two regions. Spark Performance Tuning-Learn to Tune Apache Spark Job. agg_inferred_removed = gx.aggregateMessages( While the applications that use caching can reserve a small storage (R), where data blocks are immune to evict. When your objects are still too large to efficiently store despite this tuning, a much simpler way to reduce memory usage is to store them in, form, using the serialized StorageLevels in the. We will also learn about Spark Data Structure Tuning, Spark Data Locality and Garbage Collection Tuning in Spark in this Spark performance tuning and Optimization tutorial. 1. rules implemented: As a result, there will be only one object per RDD partition. Note that the size of a decompressed block is often 2 or 3 times the size of the block. Keeping you updated with latest technology trends, Join DataFlair on Telegram. Apache Spark installation in the Standalone mode. ###################################################################, # create initial edges set without self loops the better choice is to cache fewer objects than to slow down task execution. # final_flag: True, False, for this id if True then proceed, otherwise only send False .withColumn(“_removed”,f.when(f.col(“removed”).isNotNull(),True).otherwise(False)) It stores each character as two bytes because of String’s internal usage of UTF-16 encoding. This can be achieved by lowering spark.memory.fraction. This type of join is best suited for large data sets, but is otherwise computationally expensive because it must first sort the left and right sides of data before merging them. In Part 3 of this series about Apache Spark on YARN, learn about improving performance and increasing speed through partition tuning in a Spark application. These performance factors include: how your data is stored, how the cluster is configured, and the operations that are used when processing the data. #remember_agg.show() Or we can decrease the size of young generation i.e., lowering –Xmn. In garbage collection statistics, if OldGen is near to full we can reduce the amount of memory used for caching. .withColumn(“_scrap_date”,f.when(f.col(“_scrap_date”).isNull(),f.col(“agg_scrap_date”)).otherwise(f.col(“_scrap_date”))) .withColumn(“_inferred_removed”,f.when(f.col(“removed”).isNotNull(),True).otherwise(f.col(“_inferred_removed”))) \ Informatica Interview Questions: Over the years, the data warehousing ecosystem has changed. to change the default. To use the full cluster the level of parallelism of each program should be high enough. If used properly, tuning can: It is the process of converting the in-memory object to another format that can be used to store in a file or send over the network. I have found four most important parameters that will help in tuning spark's performance. Objective. Refer this guide to learn the Apache Spark installation in the Standalone mode. The goal of GC tuning in Spark is to ensure that only. A SQL Server index is considered as one of the most important factors in the performance tuning process. How Fault Tolerance is achieved in Apache Spark, groupByKey and other Transformations and Actions API in Apache Spark with examples, Apache Spark Interview Questions and Answers. # 1) Prepare input data for IR algorithm # this is a comon workaround in Spark to find empty dataframes Parquet arranges data in columns, putting related values in close proximity to each other to optimize query performance, minimize I/O, and facilitate compression. # the max_iter limit is a limit if the algorithm is not converging at all to stop and break out the loop break, # Cache dataframe sendToSrc=msgToSrc_removed, f.when((f.col(“agg_inferred_removed”)==True) & (f.col(“agg_removed”)==True)& (f.col(“_size”)>1),True) Is there an API for implementing graphs in Spark? It is because the data travel between processes is quite slower than PROCESS_LOCAL. This is done only until storage memory usage falls under certain threshold R. We can get several properties by this design. Spark prefers to schedule all tasks at the best locality level, but this is not always possible. It is flexible but slow and leads to large serialized formats for many classes. These findings (or discoveries) usually fall into a study category than a single topic and so the goal of Spark SQL’s Performance Tuning Tips and Tricks chapter is to have a single place for the so-called tips and tricks. msgToSrc_id = AM.dst[“id”] Apache Spark Interview Questions and Answers. The size of each serialized task reduces by using broadcast functionality in SparkContext. Batch and Window Sizes – The most common question is what minimum batch size Spark Streaming can use. f.max(AM.msg).alias(“agg_removed”), agg_scrap_date = gx.aggregateMessages( Sometimes to decrease memory usage RDDs are stored in serialized form. You can change the join type in your configuration by setting spark.sql.autoBroadcastJoinThreshold, Three main features in adaptive execution, There are three considerations in tuning memory usage: the. I do not find out what I do wrong with caching or the way of iterating. The performance of your Apache Spark jobs depends on multiple factors. But if the two are separate, then either the code should be moved to data or vice versa. .drop("final_flag") In this article “Kafka Performance tuning”, we will describe the configuration we need to take care in setting up the cluster configuration. #######################################################################################. This blog also covers what is Spark SQL performance tuning and various factors to tune the Spark SQL performance in Apache Spark.Before reading this blog I would recommend you to read Spark Performance Tuning. Thus, it is better to use a data structure in Spark with lesser objects. So, You still have an opportunity to move ahead in your career in Apache Spark Development. if((iter_>0) & (len(full_agg.select(“id”,”final_flag”).subtract(remember_agg.select(“id”,”final_flag”)).take(1))==0)): Generally, it considers the tasks that are about 20 Kb for optimization. 20. In situations where there is no unprocessed data on any idle executor, Spark switches to lower locality levels. First, the application can use entire space for execution if it does not use caching. If there are too many minor collections but not many major GCs, allocating more memory for Eden would help. Thus, can be achieved by adding -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps to Java option. Guarantees that jobs are on correct execution engine. StructField(“final_flag”,BooleanType(),True), I have lined up the questions as below. Spark SQL’s Performance Tuning Tips and Tricks (aka Case Studies) From time to time I’m lucky enough to find ways to optimize structured queries in Spark SQL. f.when((f.col(“agg_inferred_removed”)==True) & (f.col(“agg_removed”)==False),True) The Spark SQL performance can be affected by some tuning consideration. ################################################################ “””, _logger.warning(“+++ find_inferred_removed(): starting inferred_removed analysis …”), #################################################################### # to find out if nothing is more todo substract the remember_agg from the current agg dataframe In case our objects are large we need to increase spark.kryoserializer.buffer config. The reasons for such behavior are: By avoiding the Java features that add overhead we can reduce the memory consumption. We can switch to Karyo by initializing our job with SparkConf and calling- #print(“###########”) ) , so that each task’s input set is smaller. Spark Interview Questions. 5) skip self loops an array of, You can pass the level of parallelism as a second argument (see the, documentation), or set the config property. # This will be a real copy, a new RDD, immutable?? If a task uses a large object from driver program inside of them, turn it into the broadcast variable. Spark Performance Tuning is the process of adjusting settings to record for memory, cores, and instances used by the system. No, it doesn’t provide storage layer but it lets you use many data sources. The Survivor regions are swapped. In our last Kafka Tutorial, we discussed Kafka load test. Just as the number of reducers is an important parameter in tuning MapReduce jobs, tuning the number of partitions at stage boundaries can often make or break an application’s performance. #     min(True,True)=True -> only true if all true #     min(True,False)=False –> otherwise false .withColumn(“_inferred_removed”,f.when(f.col(“final_flag”)==True,True).otherwise(f.col(“_inferred_removed”))) 250+ Spark Sql Programming Interview Questions and Answers, Question1: What is Shark? Python Version: 3.7 If you're looking for Apache Spark Interview Questions for Experienced or Freshers, you are at right place. Indexes are created to speed up the data retrieval and the query processing operations from a database table or view, by providing swift access to the database table rows, without the need to scan all the table’s data, in order to retrieve the requested data. For an object with very little data in it (say one, Collections of primitive types often store them as “boxed” objects such as. _logger.warning(“+++ find_inferred_removed(): iteration step= ” + str(iter_+1) + ” with loop time= ” + str(round(time.time()-loop_start_time)) + ” seconds”) 2) stop on removed.inNotNull() – either removed is Null or it contains the timestamp of removal # id will be the id 6) handle rebuilds as combination of binary split and removed.inNotNull() To maximize the opportunity to get to know your candidates, here are 10 telling interview questions to ask in your next interview: 1. It is faster to move serialized code from place to place then the chunk of data because the size of the code is smaller than the data. Memory usage in Spark largely falls under one of two categories: The best way to size the amount of memory consumption a dataset will require is to create an RDD, put it into cache, and look at the “Storage” page in the web UI. Snappy also gives reasonable compression with high speed. Spark performance is very important concept and many of us struggle with this during deployments and failures of spark applications. # this removes real self loops and also cycles which are in the super_edge notation also self loops Where does Spark Driver run on Yarn? It plays a distinctive role in the performance of any distributed application. In general, 500 milliseconds has proven to be a good minimum size for many applications. for iter_ in range(max_iter): If your tasks use any large object from the driver program inside of them (e.g. 1) start from scrap=true backwards sc.emptyRDD(), Performance Interview Questions and Answers. .where(f.col(“src”)!=f.col(“dst”)) Best Apache Spark Interview Questions and Answers. Avoid the nested structure with lots of small objects and pointers. f.max(AM.msg).alias(“agg_scrap_date”), We can fix this by increasing the level of parallelism so that each task’s input set is small. Your email address will not be published. 12. According to research, Oracle Performance Tuning has a market share of about 40.3%. .join(agg_scrap_date,agg_inferred_removed.id==agg_scrap_date.id,how=”left”) — 23/05/2016 By default, Spark uses the SortMerge join type. agg_id = gx.aggregateMessages( Ensure proper use of all resources in an effective manner. Execution can drive out the storage if necessary. .withColumn(“final_flag”, Monday, February 27, 2017. # _inferred_removed: always True if scrap=True or removed=True As we know Apache Spark is a booming technology nowadays. loop_start_time =time.time() RACK_LOCAL data is on the same rack of the server. Both execution and storage share a unified region M. When the execution memory is not in use, the storage can use all the memory. According to research Apache Spark has a market share of about 4.9%. Spark SQL is a highly scalable and efficient relational processing engine with ease-to-use APIs and mid-query fault tolerance. GraphX is the Spark API for graphs and graph-parallel computation. Also, I have read spark's performance tuning docs but increasing the batchsize, and queryTimeout have not seemed to improve performance. For example. By default, Java objects are fast to access, but can easily consume a factor of 2-5x more space than the “raw” data inside their fields. sendToSrc=msgToSrc_scrap_date, Although RDDs fit in our memory many times we come across a problem of OutOfMemoryError. If you're looking for Oracle Performance Tuning Interview Questions for Experienced or Freshers, you are at the right place. Spark performance tuning checklist, by Taraneh Khazaei — 08/09/2017 Apache Spark as a Compiler: Joining a Billion Rows per Second on a Laptop , by Sameer Agarwal et al. ), # Cache dataframe I Hope these Performance Testing Interview Questions will help you in your interviews. It provides the ability to read from almost every popular file systems such as HDFS, Cassandra, Hive, HBase, SQL servers. You can share your queries about Spark performance tuning, by leaving a comment. # the latest value of the _to_remove flag of each edge is send backwards to be compared Thank you!! Thus, it extends the Spark RDD with a Resilient Distributed Property Graph. In garbage collection, tuning in Apache Spark, the first step is to gather statistics on how frequently garbage collection occurs. _logger.warning(“+++ find_inferred_removed(): THE END: Inferred removed analysis completed after ” + str(iter_+1) + ” iterations in ” + str(round(time.time()-loop_start_time)) + ” seconds”) Scala, the Unrivalled Programming Language with its phenomenal capabilities in handling Petabytes of Big-data with ease. In case you have attended any interviews in the recent past, do paste those interview questions in the comments section and we’ll answer them. Effective changes are made to each property and settings, to ensure the correct usage of resources based on system-specific setup. (I tried calling df.cache() in my script before df.write, but runtime for the script was still 4hrs) Additionally, my aws emr hardware setup and spark-submit are: Master Node (1): m4.xlarge. #     min(False,False)=False, # AM.msg: So hole ich mir die Nachricht die kommt 1. We use the registerKryoClasses method, to register our own class with Kryo. #full_agg.show() , so using data structures with fewer objects (e.g. ]) It also gathers the amount of time spent in garbage collection. It also aims at the size of a young generation which is enough to store short-lived objects. ANY data resides somewhere else in the network and not in the same rack. .withColumn(“_scrap_date”,f.when(f.col(“scrap”)==True,f.col(“created_utc_last”)).otherwise(None)) Keeping you updated with latest technology trends. Consequently, to increase the performance of the system performance tuning plays the vital role. Memory issue?? .select(“agg_1.id”,”final_flag”,”agg_scrap_date”) ), # set result set to initial values Apache Parquet gives the fastest read performance with Spark. This page will let us know the amount of memory RDD is occupying. # initialize the values with true if the inferred_removed or the scrap column has true value We can increase the number of cores in our cluster because Spark reuses one executor JVM across many tasks and has low task launching cost. We consider Spark memory management under two categories: execution and storage. performance tuning in spark streaming. The only downside of storing data in serialized form is slower access times, due to having to deserialize each object on the fly. What Spark typically does is wait a bit in the hopes that a busy CPU frees up. Improves the performance time of the system. Kryo serialization – To serialize objects, Spark can use the Kryo library (Version 2). agg_inferred_removed.alias(“agg_1″) ) Spark Interview Questions – Spark Libraries Learn Spark Streaming For Free>> 11. f.min(AM.msg).alias(“agg_inferred_removed”), So, this blog will definitely help you regarding the same. If an object is old enough or Survivor2 is full, it is moved to Old. If data and the code that operates on it are together then computation tends to be fast. Spark can efficiently support tasks as short as 200 ms, because it reuses one executor JVM across many tasks and it has a low task launching cost, so you can safely increase the level of parallelism to more than the number of cores in your clusters. ) gx=GraphFrame(vertices,cachedNewEdges), Your email address will not be published. When reading CSV and JSON files, you will get better performance by specifying the schema, instead of using inference; specifying the schema reduces errors for data types and is recommended for production code. The process of adjusting settings to record for memory, cores, and instances used by the system is termed tuning. # send the own id backwards (in order to check of multi splits) But the key point is that cost of garbage collection in Spark is proportional to a number of Java objects. # create initial graph object # create temporary working column _to_remove that holds the values during iteration through the graph Even though we have two relevant configurations, the users need not adjust them. 15+ Apache Spark best practices, memory mgmt & performance tuning interview FAQs – Part-1 Posted on August 1, 2018 by There are so many different ways to solve the big data problems at hand in Spark, but some approaches can impact on performance, and lead to performance and memory issues. This process guarantees that the Spark has optimal performance and prevents resource bottlenecking in Spark. ), # break condition: if nothing more to aggregate quit the loop .drop("id") A Broadcast join is best suited for smaller data sets, or where one side of the join is much smaller than the other side. If full garbage collection is invoked several times before a task is complete this ensures that there is not enough memory to execute the task. cachedNewEdges = AM.getCachedDataFrame(result_edges) sendToSrc=msgToSrc_inferred_removed, edges # exclude self loops There are several ways to achieve this: JVM garbage collection is problematic with large churn RDD stored by the program. Based on data current location there are various levels of locality. Data Locality. Collections of primitive types often store them as “boxed objects”. We will be happy to solve them. StructField(“scrap_date”,TimestampType(),True) Serializing the data plays an important role in tuning the system. What is proactive tuning and reactive tuning? Yes , really nice information. .join(agg_id,agg_inferred_removed.id==agg_id.id,how=”left”) # send nothing to destination vertices There are a lot of opportunities from many reputed companies in the world. Finally when Old is close to full, a full GC is invoked. November, 2017 adarsh Leave a comment. The value should be large so that it can hold the largest object we want to serialize. Parquet stores data in columnar format, and is highly optimized in Spark. 3) stop on binary split Deep Dive into Spark SQL with Advanced Performance Tuning Download Slides . Spark prints the serialized size of each task on the master, so you can look at that to decide whether your tasks are too large; in general tasks larger than about 20 KB are probably worth optimizing. These Apache Spark questions and answers are suitable for both fresher’s and experienced professionals at any level. Data serialization plays important role in good network performance and can also help in reducing memory usage, and memory tuning. They are as follows: spark.memory. .join(remember_agg,result_edges.dst==remember_agg.id,how=”left”) See Also-Spark SQL Optimization Apache Spark Interview Questions and Answers; Reference for Spark These logs will be in worker node, not on drivers program. Thus, Performance Tuning guarantees the better performance of the system. If we want to know the size of Spark memory consumption a dataset will require to create an RDD, put that RDD into the cache and look at “Storage” page in Web UI. Spark performance is very important concept and many of us struggle with this during deployments and failures of spark applications. Before or when writing a DataFrame, you can use dataframe.coalesce(N) to reduce the number of partitions in a DataFrame, without shuffling, or df.repartition(N) to reorder and either increase or decrease the number of partitions with shuffling data across the network to achieve even load balancing. There are two options: a) wait until a busy CPU frees up to start a task on data on the same server, or b) immediately start a new task in a farther away place that requires moving data there. # message that sends the _to_remove flag backwards in the graph to the source of each edge Required fields are marked *, Home About us Contact us Terms and Conditions Privacy Policy Disclaimer Write For Us Success Stories, This site is protected by reCAPTCHA and the Google. Hope you like our explanation. Each question has the detailed answer, which will make you confident to face the interviews of Apache Spark. I am running in heavy performance issues in a interative algorithm using the graphframes framework with message aggregation. There are about 40 bytes of overhead over the raw string data in Java String. # Inferred Removed detection using graphframe message aggregation Spark employs a number of optimization techniques to cut the processing time. If there are 10 characters String, it can easily consume 60 bytes. (if you have high turnover in terms of objects). Data locality can have a major impact on the performance of Spark jobs. # aggregate with the min function over boolean. Instead of using strings for keys, use numeric IDs or enumerated objects. sendToDst=None) Scala Interview Questions: Beginner Level # send scrap_date=utc_created_last from scraped edge backwards (in order to stop on newer edges) msgToSrc_inferred_removed = AM.edge[“_inferred_removed”] .otherwise( . If a full GC is invoked multiple times for before a task completes, it means that there isn’t enough memory available for executing tasks. Although it is more compact than Java serialization, it does not support all Serializable types. Apache Spark has in-memory computation nature. If we want to know the memory consumption of particular object, use SizeEstimator’S estimate method. If you really want to spark a more authentic — and revealing — discussion, the answer is simple: ask better questions. You can share your queries about Spark performance tuning, by leaving a comment. agg_removed = gx.aggregateMessages( You can set the size of the Eden to be an over-estimate of how much memory each task will need. gx=GraphFrame(vertices,edge_init), #################################################################### After learning performance tuning in Apache Spark, Follow this guide to learn How Apache Spark works in detail. The computation gets slower due to formats that are slow to serialize or consume a large number of files. Commonly, scenario-based interview questions present a situation and ask the person being interviewed to speak about what they need to do to solve the problem. The order from closest to farthest is: So, this was all in Spark Performance Tuning. The best approach is to start with a larger batch size (around 10 seconds) and work your way down to a smaller batch size. Graphframes Version: 0.7.0, ####################################################################################### To represent our data efficiently, it uses the knowledge of types very effectively. This has been a short guide to point out the main concerns you should know about when tuning a Spark application – most importantly, data serialization and memory tuning. The simplest fix here is to. msgToSrc_removed = AM.edge[“_removed”] #print(“###########”) Since the data is on the same rack but on the different server, so it sends the data in the network, through a single switch. Also, we will discuss Tuning Kafka Producers, Tuning Kafka Consumers, and Tuning Kafka Brokers.So, let’s start with Kafka Performance Tuning. can greatly reduce the size of each serialized task, and the cost of launching a job over a cluster. This is because the working set of our task say groupByKey is too large. With this, we have come to the end of Performance Testing interview questions article. Hadoop and Programming Interview Questions. Configuration of in-memory caching can be done using the setConf method on SparkSession or by runningSET key=valuec… With this, we can avoid full garbage collection to gather temporary object created during task execution. In general, we recommend 2-3 tasks per CPU core in your cluster. This process guarantees that the Spark has optimal performance and prevents resource bottlenecking. We’ll delve deeper into how to tune this number in a later section. The level of parallelism can be passed as a second argument. .join(agg_removed,agg_inferred_removed.id==agg_removed.id,how=”left”) result_edges=edge_init, # this is the temporary dataframe where we write in the aggregation results each round # _removed: True if removed .withColumn(“_inferred_removed”,f.when(f.col(“_scrap_date”)

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