Yes, it is possible to run Spark and Mesos with Hadoop by launching each of these as a separate service on the machines. Mesos acts as a unified scheduler that assigns tasks to either Spark or Hadoop.
Spark is intellectual in the manner in which it operates on data. When you tell Spark to operate on a given dataset, it heeds the instructions and makes a note of it, so that it does not forget - but it does nothing, unless asked for the final result.
When a trformation like map () is called on a RDD-the operation is not performed immediately. Trformations in Spark are not evaluated till you perform an action. This helps optimize the overall data processing workflow.
Use the subtractByKey () function.
An RDD that consists of row objects (wrappers around basic string or integer arrays) with schema information about the type of data in each column.
Developers often make the mistake of:-
persist () allows the user to specify the storage level where as cache () uses the default storage level.
Minimizing data trfers and avoiding shuffling helps write spark programs that run in a fast and reliable manner.
The various ways in which data trfers can be minimized when working with Apache Spark are:
Spark MLib- Machine learning library in Spark for commonly used learning algorithms like clustering, regression, classification, etc.
Spark Streaming – This library is used to process real time streaming data.
Spark GraphX – Spark API for graph parallel computations with basic operators like joinVertices, subgraph, aggregateMessages, etc.
Spark SQL – Helps execute SQL like queries on Spark data using standard visualization or BI tools.
Stateless Trformations:- Processing of the batch does not depend on the output of the previous batch.
Examples: map (), reduceByKey (), filter ().
Stateful Trformations:- Processing of the batch depends on the intermediary results of the previous batch.
Examples: Trformations that depend on sliding windows.
Sliding Window controls trmission of data packets between various computer networks. Spark Streaming library provides windowed computations where the trformations on RDDs are applied over a sliding window of data. Whenever the window slides, the RDDs that fall within the particular window are combined and operated upon to produce new RDDs of the windowed DStream.
Discretized Stream is a sequence of Resilient Distributed Databases that represent a stream of data. DStreams can be created from various sources like Apache Kafka, HDFS, and Apache Flume.
DStreams have two operations: –
Lineage graphs are always useful to recover RDDs from a failure but this is generally time consuming if the RDDs have long lineage chains. Spark has an API for check pointing i.e. a REPLICATE flag to persist. However, the decision on which data to checkpoint - is decided by the user. Checkpoints are useful when the lineage graphs are long and have wide dependencies.
It has all the basic functionalities of Spark, like - memory management, fault recovery, interacting with storage systems, scheduling tasks, etc.
It renders scalable partitioning among various Spark instances and dynamic partitioning between Spark and other big data frameworks.
Yes, Apache Spark can be run on the hardware clusters managed by Mesos.
These are read only variables, present in-memory cache on every machine. When working with Spark, usage of broadcast variables eliminates the necessity to ship copies of a variable for every task, so data can be processed faster. Broadcast variables help in storing a lookup table inside the memory which enhances the retrieval efficiency when compared to an RDD lookup ().
Yes, it is possible if you use Spark Cassandra Connector.
Spark engine schedules, distributes and monitors the data application across the spark cluster.
Trformations are functions executed on demand, to produce a new RDD. All trformations are followed by actions. Some examples of trformations include map, filter and reduceByKey.
Actions are the results of RDD computations or trformations. After an action is performed, the data from RDD moves back to the local machine. Some examples of actions include reduce, collect, first, and take.
Pinterest, Conviva, Shopify, Open Table
Apache Spark automatically persists the intermediary data from various shuffle operations, however it is often suggested that users call persist () method on the RDD in case they plan to reuse it. Spark has various persistence levels to store the RDDs on disk or in memory or as a combination of both with different replication levels.
The various storage/persistence levels in Spark are:
No. Apache Spark works well only for simple machine learning algorithms like clustering, regression, classification.
No , it is not necessary because Apache Spark runs on top of YARN.
Using SIMR (Spark in MapReduce) users can run any spark job inside MapReduce without requiring any admin rights.
Spark uses Akka basically for scheduling. All the workers request for a task to master after registering. The master just assigns the task. Here Spark uses Akka for messaging between the workers and masters.
Spark has a web based user interface for monitoring the cluster in standalone mode that shows the cluster and job statistics. The log output for each job is written to the work directory of the slave nodes.
RDDs (Resilient Distributed Datasets) are basic abstraction in Apache Spark that represent the data coming into the system in object format. RDDs are used for in-memory computations on large clusters, in a fault tolerant manner. RDDs are read-only portioned, collection of records, that are –
Immutable – RDDs cannot be altered.
Resilient – If a node holding the partition fails the other node takes the data.
Special operations can be performed on RDDs in Spark using key/value pairs and such RDDs are referred to as Pair RDDs. Pair RDDs allow users to access each key in parallel. They have a reduceByKey () method that collects data based on each key and a join () method that combines different RDDs together, based on the elements having the same key.
Apache Spark stores data in-memory for faster model building and training. Machine learning algorithms require multiple iterations to generate a resulting optimal model and similarly graph algorithms traverse all the nodes and edges.
These low latency workloads that need multiple iterations can lead to increased performance. Less disk access and controlled network traffic make a huge difference when there is lots of data to be processed.
To connect Spark with Mesos:
Catalyst framework is a new optimization framework present in Spark SQL. It allows Spark to automatically trform SQL queries by adding new optimizations to build a faster processing system.
Most of the data users know only SQL and are not good at programming. Shark is a tool, developed for people who are from a database background - to access Scala MLib capabilities through Hive like SQL interface. Shark tool helps data users run Hive on Spark - offering compatibility with Hive metastore, queries and data.
BlinkDB is a query engine for executing interactive SQL queries on huge volumes of data and renders query results marked with meaningful error bars. BlinkDB helps users balance ‘query accuracy’ with response time.
Apache spark does not scale well for compute intensive jobs and consumes large number of system resources. Apache Spark’s in-memory capability at times comes a major roadblock for cost efficient processing of big data. Also, Spark does have its own file management system and hence needs to be integrated with other cloud based data platforms or apache hadoop.
You can trigger the clean-ups by setting the parameter ‘spark.cleaner.ttl’ or by dividing the long running jobs into different batches and writing the intermediary results to the disk.
The 3 different clusters managers supported in Apache Spark are:
Driver: The process that runs the main () method of the program to create RDDs and perform trformations and actions on them.
Executor: The worker processes that run the individual tasks of a Spark job.
Cluster Manager: A pluggable component in Spark, to launch Executors and Drivers. The cluster manager allows Spark to run on top of other external managers like Apache Mesos or YARN.
Apache Spark is mainly used for:
Scala, Java, Python, R and Clojure
Data storage model in Apache Spark is based on RDDs. RDDs help achieve fault tolerance through lineage. RDD always has the information on how to build from other datasets. If any partition of a RDD is lost due to failure, lineage helps build only that particular lost partition.
A node that can run the Spark application code in a cluster can be called as a worker node. A worker node can have more than one worker which is configured by setting the SPARK_ WORKER_INSTANCES property in the spark-env.sh file. Only one worker is started if the SPARK_ WORKER_INSTANCES property is not defined.
Every spark application has same fixed heap size and fixed number of cores for a spark executor. The heap size is what referred to as the Spark executor memory which is controlled with the spark.executor.memory property of the –executor-memory flag.
Every spark application will have one executor on each worker node. The executor memory is basically a measure on how much memory of the worker node will the application utilize.
A sparse vector has two parallel arrays –one for indices and the other for values. These vectors are used for storing non-zero entries to save space.
Spark need not be installed when running a job under YARN or Mesos because Spark can execute on top of YARN or Mesos clusters without affecting any change to the cluster.
Hadoop MapReduce requires programming in Java which is difficult, though Pig and Hive make it considerably easier. Learning Pig and Hive syntax takes time. Spark has interactive APIs for different languages like Java, Python or Scala and also includes Shark i.e. Spark SQL for SQL lovers - making it comparatively easier to use than Hadoop.