Beyond HDFS and MapReduce: Exploring Hadoop’s Family of Tools#

HDFS Overview#

The Hadoop Distributed File System (HDFS) is the storage layer that powers the Hadoop ecosystem. It breaks large files into smaller blocks (defaulting to 128 MB or 256 MB in modern Hadoop distributions) which are then distributed across a cluster of machines. HDFS is designed to handle large datasets with high throughput and fault tolerance:

• Replication: Each block is typically replicated on multiple data nodes for fault tolerance.
• High Throughput: Data writes and reads operate at cluster scale, allowing for processing of petabytes of data.
• Streaming Data Access: HDFS is optimized for batch processing and streaming entire files, not small file random reads.

Key concepts include:

• NameNode: The master daemon that maintains the filesystem namespace and metadata.
• DataNodes: Worker daemons that store the blocks.
• Secondary NameNode: A helper process that merges the namespace edits with the filesystem image to reduce NameNode restart time.

When using HDFS, the first step is typically to put your data into the distributed file system so that analytics frameworks can then process it in a distributed manner.

MapReduce Overview#

MapReduce is the original processing model for Hadoop. Its popularity grew in tandem with the NoSQL movement and the rise of large-scale data analytics. In the MapReduce paradigm:

1. Map Phase: Data is split into input splits. Each mapper processes a chunk of data and outputs key-value pairs.
2. Shuffle and Sort: The framework sorts the mapping output by key and redistributes data to reducers.
3. Reduce Phase: Reducers aggregate the mapper output by key, producing the final result.

This model simplifies parallelization. Developers write map and reduce functions in languages like Java, Python, or others. Although MapReduce can be very powerful, it’s relatively slow compared to more modern frameworks like Apache Spark, especially for iterative or real-time tasks.

Understanding YARN#

YARN (Yet Another Resource Negotiator) is the resource management layer in Hadoop. It decoupled MapReduce from the cluster resource management, enabling new processing engines (such as Spark and Tez) to run on Hadoop. YARN manages computing resources and schedules tasks, acting as an operating system for distributed systems. Major YARN components:

• ResourceManager: Allocates cluster resources and orchestrates tasks.
• NodeManager: Manages resources on each node, monitors container usage.
• ApplicationMaster: Negotiates resources from ResourceManager on behalf of a specific application.

With YARN, Hadoop clusters can run MapReduce, Spark, Hive, and more simultaneously, maximizing cluster utilization.

Flume Overview#

Apache Flume is designed for ingesting streaming data into Hadoop. Commonly used scenarios are collecting log files from diverse sources:

• Agent-Based Architecture: A Flume agent on each host captures data from sources and forwards it to channels and sinks.
• Sources: Could be log files, Syslog, or custom sources.
• Channels: Act as a temporary store (in-memory or on disk).
• Sinks: Deliver data to storage targets, frequently HDFS or HBase.

Example use cases:

• Real-time log ingestion from web servers.
• Collecting event data from IoT sensors.
• Continuous streaming of social media feeds into Hadoop for analytics.

Sqoop Overview#

Apache Sqoop is optimized for bulk data transfers between Hadoop and relational databases (RDBMS). This includes:

• Importing Data: Moves large amounts of tabular data from MySQL, PostgreSQL, Oracle, or other databases into HDFS, Hive tables, or HBase.
• Exporting Data: Transfers processed data back to RDBMS for reporting or operational use.

Typical commands look like this:

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sqoop import \
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  --connect jdbc:mysql://database_server/db_name \
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  --username user \
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  --password pass \
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  --table employees \
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  --target-dir /user/hadoop/employees

Sqoop’s parallel import/export, incremental imports, and multiple connector support greatly simplify data synchronization between traditional databases and Hadoop.

Pig Overview#

Apache Pig is a scripting platform that simplifies writing data processing tasks in Hadoop. Instead of writing low-level MapReduce jobs, developers can use a scripting language called Pig Latin:

• Pig Latin: A data flow language that allows the user to specify transformations (filters, joins, group by) in sequence.
• Pig Engine: Translates Pig Latin scripts into MapReduce jobs or other processing routines under the hood.

Pig is ideal for:

• Iterative data transformations.
• ETL (Extract, Transform, Load) pipelines.
• Prototyping data operations quickly without building complex Java or Python MapReduce code.

Hive Overview#

Apache Hive provides a data warehousing solution on top of Hadoop, offering a SQL-like interface called HiveQL. It stores data in tables and supports partitioning and bucketing for efficient query execution. Key points about Hive:

• HiveQL: Similar to SQL, making it approachable for those with relational database skills.
• Schema-on-Read: You can define a schema for data upon reading it, rather than requiring an upfront schema.
• Queries Translate to MapReduce: By default, queries translate to MapReduce, though newer integrations with Tez or Spark can accelerate execution.

Hive is best suited for:

• Ad-hoc queries on large datasets.
• Data warehousing and reporting.
• Aggregations and transformations at scale.

Spark Core and RDDs#

Apache Spark is a general-purpose cluster computing system that can run on YARN, Mesos, or Kubernetes. It provides significantly faster data processing, especially for iterative workloads, thanks to in-memory computations. Key Spark concepts:

• Resilient Distributed Datasets (RDDs): Immutable, fault-tolerant collections of data. You can transform (map, filter) or perform actions (count, collect) on RDDs.
• Lineage: RDD transformations are stored in a lineage graph, making fault recovery straightforward.

Spark’s speed and flexibility make it a top choice for diverse workloads—ranging from batch jobs to real-time stream processing.

Spark SQL and DataFrames#

Spark SQL extends core Spark with a module that works with structured data:

• DataFrames: Distributed collections of data organized into named columns. Think of them like tables.
• SparkSQL: An SQL interface that can be run programmatically or interactively.
• Catalyst Optimizer: A powerful execution engine that optimizes queries for performance.

Developers increasingly prefer DataFrames for ease of use and performance gains compared to raw RDD manipulations.

Spark Streaming#

Spark supports real-time data processing via Spark Streaming (or Structured Streaming in newer versions):

• Micro-Batch Approach: Data is processed in small batches (e.g., every second).
• Receivers: Collect data from sources like Kafka, Flume, or sockets.
• Transformations: Similar to those in batch Spark.
• Output: Write to HDFS, databases, dashboards, or anywhere you like.

Structured Streaming further reduces latency and simplifies real-time pipelines by using a unified API for batch and streaming.

Defining Workflows#

Apache Oozie is a workflow scheduler system that coordinates Hadoop jobs (e.g. MapReduce, Pig, Hive, Spark) and other tasks into a single, logical data processing pipeline:

• Workflow: An XML definition describing nodes and transitions.
• Actions: Tasks to be executed (MapReduce, Pig, Hive, Shell, Java).
• Control Nodes: Decision, fork, join, and end nodes to handle branching or merging.

By arranging multiple tasks, you can create complex workflows that run automatically or are triggered by data availability.

Coordination and Bundles#

Oozie’s Coordination and Bundle features add time- or data-based triggers to your workflows:

• Coordinator: Defines frequency-based or data-availability-based triggers to start workflows.
• Bundle: A set of coordinators, giving you a higher-level grouping of data pipelines.

This layering helps orchestrate end-to-end data pipelines that run daily, hourly, or whenever new data arrives.

HBase for NoSQL#

Apache HBase is a NoSQL column-oriented database that runs on top of HDFS. It provides near real-time read/write access to large datasets:

• Column-Family Storage: Data is grouped by column families.
• Row Key: Each record is identified by a unique row key, enabling fast lookups.
• High Write Throughput: HBase handles large-scale write-intensive workloads well.

Use cases include:

• Time-series data storage (sensor data, log data)
• Operational analytics with quick lookups
• Serving layer for interactive Hadoop applications

Kafka for Real-Time Data Pipelines#

Apache Kafka is a distributed streaming platform that helps build real-time data pipelines:

• Publish-Subscribe Model: Producers publish events to topics, consumers subscribe to those topics.
• High Throughput: Kafka is designed to handle very large event streams with minimal latency.
• Persistent Storage: Messages are retained on disk, providing replay capability.

Kafka integrates nicely with Spark Streaming, Flume, and other Hadoop ecosystem components to handle real-time feeds.

ZooKeeper for Distributed Coordination#

ZooKeeper is a centralized service for maintaining configuration information, naming, distributed synchronization, and group services:

• Leader Election: Helps determine a master in distributed systems.
• Configuration Management: Stores and watches for updates to configurations.
• Metadata Management: Minimizes the complexity of building robust distributed applications.

Tools like HBase, Kafka, and Oozie rely heavily on ZooKeeper for reliable distributed operations.

Basic HDFS Commands#

Below is a simple illustration of commonly used HDFS shell commands:

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# List files in HDFS directory
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hdfs dfs -ls /user/hadoop/
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# Put (upload) a local file into HDFS
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hdfs dfs -put local_dataset.csv /user/hadoop/datasets/
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# Get (download) a file from HDFS to local filesystem
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hdfs dfs -get /user/hadoop/datasets/input_data.txt ./local_folder/
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# Remove a file or directory from HDFS
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hdfs dfs -rm /user/hadoop/datasets/old_data.txt
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# Create a directory in HDFS
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hdfs dfs -mkdir /user/hadoop/new_folder

Setting Up a Simple MapReduce Job#

A very simple Java-based MapReduce job might look like this:

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import org.apache.hadoop.conf.Configuration;
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import org.apache.hadoop.fs.Path;
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import org.apache.hadoop.io.IntWritable;
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import org.apache.hadoop.io.Text;
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import org.apache.hadoop.mapreduce.Job;
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import org.apache.hadoop.mapreduce.Mapper;
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import org.apache.hadoop.mapreduce.Reducer;
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import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
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import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
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import java.io.IOException;
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public class WordCount {
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    public static class TokenizerMapper
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         extends Mapper<Object, Text, Text, IntWritable> {
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        private final static IntWritable one = new IntWritable(1);
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        private Text word = new Text();
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        public void map(Object key, Text value, Context context)
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                throws IOException, InterruptedException {
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            String[] tokens = value.toString().split("\\s+");
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            for(String token: tokens) {
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                word.set(token);
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                context.write(word, one);
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            }
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        }
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    }
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    public static class IntSumReducer
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         extends Reducer<Text, IntWritable, Text, IntWritable> {
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        public void reduce(Text key, Iterable<IntWritable> values, Context context)
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                throws IOException, InterruptedException {
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            int sum = 0;
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            for(IntWritable val : values) {
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                sum += val.get();
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            }
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            context.write(key, new IntWritable(sum));
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        }
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    }
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    public static void main(String[] args) throws Exception {
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        Configuration conf = new Configuration();
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        Job job = Job.getInstance(conf, "word count");
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        job.setJarByClass(WordCount.class);
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        job.setMapperClass(TokenizerMapper.class);
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        job.setReducerClass(IntSumReducer.class);
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        job.setOutputKeyClass(Text.class);
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        job.setOutputValueClass(IntWritable.class);
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        FileInputFormat.addInputPath(job, new Path(args[0]));
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        FileOutputFormat.setOutputPath(job, new Path(args[1]));
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        System.exit(job.waitForCompletion(true) ? 0 : 1);
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    }
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}

Compile and run this MapReduce job against your data in HDFS using:

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hadoop jar wordcount.jar WordCount /input_data /output_data

Pig Script Example#

Below is an example Pig Latin script that calculates the average score of students by subject:

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-- Load data from HDFS
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student_scores = LOAD '/user/hadoop/scores.csv'
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                  USING PigStorage(',')
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                  AS (student_id:chararray, subject:chararray, score:int);
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-- Group by subject
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grouped_scores = GROUP student_scores BY subject;
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-- Calculate average
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avg_scores = FOREACH grouped_scores GENERATE
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             group AS subject,
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             AVG(student_scores.score) AS average_score;
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-- Store results
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STORE avg_scores INTO '/user/hadoop/avg_scores_out' USING PigStorage(',');

After saving this script as avg_scores.pig, run it with:

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pig avg_scores.pig

Hive Query Example#

Assume you have a Hive table called transactions with fields (id, user_id, amount, ts). You can run a HiveQL query like:

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CREATE TABLE IF NOT EXISTS transactions (
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    id STRING,
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    user_id STRING,
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    amount DOUBLE,
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    ts STRING
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)
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ROW FORMAT DELIMITED
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FIELDS TERMINATED BY ','
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STORED AS TEXTFILE;
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LOAD DATA INPATH '/user/hadoop/transactions.csv' INTO TABLE transactions;
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SELECT user_id, SUM(amount) AS total_spent
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FROM transactions
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GROUP BY user_id
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ORDER BY total_spent DESC
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LIMIT 10;

The above query calculates total spending per user and lists the top ten.

Spark Code Example#

A quick Python example for Spark to compute word counts:

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from pyspark.sql import SparkSession
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spark = SparkSession.builder \
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    .appName("WordCountExample") \
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    .getOrCreate()
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# Read text file from HDFS
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text_rdd = spark.sparkContext.textFile("hdfs://path/to/input.txt")
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# Transformations
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words = text_rdd.flatMap(lambda line: line.split(" "))
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word_pairs = words.map(lambda word: (word, 1))
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word_counts = word_pairs.reduceByKey(lambda a, b: a + b)
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# Sort by count descending
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sorted_counts = word_counts.map(lambda x: (x[1], x[0])) \
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                           .sortByKey(False) \
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                           .map(lambda x: (x[1], x[0]))
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# Collect or save results
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for (word, count) in sorted_counts.collect():
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    print(f"{word}: {count}")
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spark.stop()

Oozie Workflow Example#

A very simple Oozie workflow workflow.xml might look like:

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<workflow-app name="sample-wf" xmlns="uri:oozie:workflow:0.5">
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    <start to="mr-node"/>
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    <action name="mr-node">
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        <map-reduce>
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            <job-tracker>${jobTracker}</job-tracker>
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            <name-node>${nameNode}</name-node>
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            <configuration>
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                <property>
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                    <name>mapred.input.dir</name>
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                    <value>${inputDir}</value>
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                </property>
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                <property>
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                    <name>mapred.output.dir</name>
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                    <value>${outputDir}</value>
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                </property>
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            </configuration>
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        </map-reduce>
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        <ok to="end"/>
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        <error to="fail"/>
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    </action>
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    <kill name="fail">
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        <message>Job failed, error message[${wf:errorMessage()}]</message>
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    </kill>
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    <end name="end"/>
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</workflow-app>

You would package this XML and a job.properties file specifying nameNode, jobTracker, etc., and then run:

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oozie job --config job.properties --run

Performance Tuning Advice#

1. File Formats: Use optimized file formats like Parquet or ORC for columnar storage and efficient compression.
2. Shuffle Optimization: Tune Spark or MapReduce configurations to handle large shuffle operations (e.g., memory, spill thresholds).
3. Partitioning: For Hive or Spark SQL, partition your data by high-cardinality columns to avoid full-table scans.
4. Broadcast Joins: In Spark, use broadcast joins when one dataset is small, reducing shuffle cost.

Security and Governance#

As clusters grow and handle sensitive data, set up robust security:

• Kerberos: Integration with Hadoop for authentication.
• Ranger or Sentry: Fine-grained access control for Hive, HBase, and other components.
• Encryption: Encrypt data at rest in HDFS and in transit with SSL/TLS.
• Auditing: Track user actions, job submissions, and data access logs.

Governance includes data lineage (tracking the origins of data), compliance with regulations (GDPR, HIPAA for healthcare), and auditing changes made to data.

Monitoring and Troubleshooting#

A production-grade Hadoop environment requires proactive monitoring:

• Ambari / Cloudera Manager: Web-based solutions for cluster monitoring and management.
• Logs: Collect and centralize logs from NameNode, DataNodes, YARN ResourceManager, and NodeManagers.
• Ganglia / Grafana: Metrics for memory usage, CPU utilization, and network traffic.
• Alerts: Threshold-based or anomaly detection systems that warn of potential issues in real-time.

Troubleshoot common issues:

• Out of Memory Errors: Increase container memory or optimize data structures.
• Slow Jobs: Investigate shuffle or skew issues.
• Job Failures: Check logs, job configurations, or underlying cluster resource constraints.