Moving Data In and Out of Hadoop and HDFSpptx

Moving Data In and Out of Hadoop

1. Moving Data In and Out of Hadoop It is refered as Ingress and Engress Hadoop Supports ingress and engress at a low level in HDFS and MapReduce Files can be moved in and out of Hadoop by HDFS(Writing External Data at HDFS level-Data Push) and data can be pulled from external data sources and pushed to external data sinks using MapReduce (Reading External data at MapReduce level-Data Pull).

2. Key Elements of Ingress and Engress Idempotence Aggregation Data Format Transformation Recoverability Correctness Resource Consumption and Performance Monitoring

3. Hadoop Ingress with different data sources-Log files, Semi Structured data/ Binary files, HBase Flume,Chukwa,Scribe are log collecting and distribution frameworks that uses HDFS as data sink for that log data. Flume It is a Distributed System for collecting streaming data. It’s highly customizable and supports plugin architecture.

4. Chukwa (Apache sub project to collect and store data in HDFS)

5. Scribe Purpose : Scribe is used for collecting and distributing log data across multiple nodes. Functionality : A Scribe server runs on each node and forwards logs to a central Scribe server. Reliability : Logs are persisted to a local disk if the downstream server is unreachable. Supported Data Sinks : It can store logs in various storage backends , including HDFS, NFS, and regular filesystems . 2. Difference from Other Log Collectors: Unlike Flume or Chukwa , Scribe does not pull logs automatically. Instead, the user must push log data to the Scribe server. For example, Apache logs require writing a daemon (background process) to forward logs to Scribe.

6. Technique 2: An automated mechanism to copy files into HDFS Existing tools like Flume, Scribe, and Chukwa are mainly designed for log file transportation.What if you need to transfer different file formats , such as semi-structured or binary files? Solution: The HDFS File Slurper is an open-source utility that can copy any file format into or out of HDFS. How the HDFS File Slurper Works: The HDFS File Slurper is a simple tool that automates copying files between a local directory and HDFS , and vice versa. It follows a structured five-step process: Scan : The Slurper reads files from the source directory . Determine HDFS destination : Optionally, it consults a script to determine where in HDFS the file should be placed. Write : The file is copied to HDFS. Verify : An optional verification step ensures successful transfer. Relocate file : The original file is moved to a completed directory after a successful copy.

7. Technique 3: Scheduling Regular Ingress Activities with Oozie If your data resides on a filesystem , web server, or other system , you need a way to regularly pull it into Hadoop . The challenge consists of two tasks : Importing data into Hadoop . Scheduling regular data transfers . Oozie is used to automate data ingress into HDFS . It can also trigger post-ingress activities , such as launching a MapReduce job to process the data. Oozie is an Apache project that originated at Yahoo! and acts as a workflow engine for Hadoop . Oozie’s coordinator engine can schedule tasks based on time and data triggers .

8. We want to move data from a relational database into HDFS using MapReduce while managing concurrent database connections effectively. Solution: This technique uses the DBInputFormat class to import data from a relational database into HDFS. It ensures mechanisms are in place to handle the load on the database. Key Classes: DBInputFormat : Reads data from the database via JDBC (Java Database Connectivity). DBOutputFormat : Writes data to the database. How It Works: DBInputFormat reads data from relational databases and maps it into the Hadoop ecosystem. To do this, it requires a bean representation of the table, which implements the Writable and DBWritable interfaces. The Writable interface is specific to Hadoop for handling serialization/deserialization.

9. We want to load relational data into your Hadoop cluster in an efficient, scalable, and idempotent way without the complexity of implementing custom MapReduce logic . Sqoop is a tool designed for bulk data transfer between relational databases and Hadoop. It supports importing data into HDFS, Hive, or HBase and exporting data back into relational databases. Created by Cloudera, it’s an Apache project in incubation. Importing Process:Importing data with Sqoop involves two main activities: Connecting to the Data Source: Sqoop gathers metadata and statistics from the source database. Executing the Import: A MapReduce job is launched to bring the data into Hadoop .

10. Sqoop uses connectors to interact with databases. There are two types: Common Connector : Handles regular reads and writes. Fast Connector : Uses database-specific optimizations for bulk data imports, making the process more efficient.