books: Awesome CS Books/mmoonneeyy.info by git lfs) Warehouse, ProgrammingLanguage, SoftwareEngineering, Web, AI, ServerSideApplication, Infrastructure. September ; ISBN ; pages; printed in black & white Hadoop in Practice, Second Edition provides over tested, instantly useful. Hadoop in Action, Second Edition. Chuck P. Lam, Mark W. Davis, and Ajit Gaddam. MEAP began September ; Publication in July (estimated). ISBN.
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Purchase of Hadoop in Practice, Second Edition includes free access to a . have an intermediate-level knowledge of Java—Effective Java, 2nd Edition by. Shelter Island, NY Typesetter: Gordan Salinovic. Illustrator: Martin Murtonen. Cover designer: Marija Tudor. ISBN Printed in the United. ment that transitions Hadoop into a distributed computing kernel that can support To download their free eBook in PDF, ePub, and Kindle formats, owners.
Exercising what you've learned. Technique 29 Using Avro to store multiple small binary files. Hadoop in Practice, Second Edition eBook added to cart. Understanding MapReduce 1. Writing a YARN application Integrating R and Hadoop for statistics and more Technique 13 Selecting the appropriate way to use Avro in MapReduce.
A brief introduction to HyperLogLog. Technique 71 Using HyperLogLog to calculate unique counts. Tuning, debugging, and testing 8. Measure, measure, measure. Tuning MapReduce 8. Common inefficiencies in MapReduce jobs. Technique 72 Viewing job statistics.
Technique 74 Dealing with a large number of input splits. Technique 77 Blazingly fast sorting with binary comparators. Technique 78 Tuning the shuffle internals. Technique 79 Too few or too many reducers. Technique 80 Using stack dumps to discover unoptimized user code.
Technique 81 Profiling your map and reduce tasks. Debugging 8. Accessing container log output. Technique 82 Examining task logs. Accessing container start scripts.
Technique 83 Figuring out the container startup command. Technique 84 Force container JVMs to generate a heap dump. MapReduce coding guidelines for effective debugging. Technique 85 Augmenting MapReduce code for better debugging.
Testing MapReduce jobs 8. Essential ingredients for effective unit testing. Technique 87 Heavyweight job testing with the LocalJobRunner. SQL on Hadoop 9. Hive 9. Hive basics. Technique 89 Working with text files. Technique 90 Exporting data to local disk. User-defined functions in Hive. Impala 9. Impala vs. Technique 95 Working with Parquet. Technique 96 Refreshing metadata.
User-defined functions in Impala.
Spark SQL 9. Spark Technique 99 Language-integrated queries. Writing a YARN application Fundamentals of building a YARN application The mechanics of a YARN application. Technique A bare-bones ApplicationMaster. Technique Running the application and accessing logs. Technique Debugging using an unmanaged application master. Additional YARN application capabilities RPC between components. Checkpointing application progress.
YARN programming abstractions Appendix A: Installing Hadoop and friends A. Code for the book. Integrating R and Hadoop for statistics and more Comparing R and MapReduce integrations. R and streaming Streaming and map-only R. Technique Calculate the daily mean for stocks.
Streaming, R, and full MapReduce. Technique Calculate the cumulative moving average for stocks. Predictive analytics with Mahout Using recommenders to make product suggestions Visualizing similarity metrics. Technique Item-based recommenders using movie ratings. Classification Writing a homemade naive Bayesian classifier. A scalable spam-detection classification system. Technique Using Mahout to train and test a spam classifier.
Additional classification algorithms.
Clustering with K-means A gentle introduction. Technique K-means with a synthetic 2D dataset. Other Mahout clustering algorithms. About the book It's always a good time to upgrade your Hadoop skills! Readers need to know a programming language like Java and have basic familiarity with Hadoop. About the author Alex Holmes works on tough big-data problems. Hadoop in Practice, Second Edition combo added to cart.
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Java 8 in Action Lambdas, streams, and functional-style programming. Elasticsearch in Action. Big Data Principles and best practices of scalable realtime data systems. Nathan Marz and James Warren.
Streaming Data Understanding the real-time pipeline. Andrew G. Storm Applied Strategies for real-time event processing. Sean T. Spark in Action. Real-World Machine Learning. Henrik Brink, Joseph W. Hadoop in Action. Chuck Lam. Nina Zumel and John Mount. Event Streams in Action. Alexander Dean, Valentin Crettaz. Git in Practice. All rights reserved. Setting up SSH for a Hadoop cluster 2. Define a common account. Distribute public key and validate logins.
Running Hadoop 2. Local standalone mode. Running Hadoop in the cloud 2. Introducing Amazon Web Services. Securing the Hadoop Platform 3. Hadoop Security Weaknesses 3.
Top 10 Security and Privacy Challenges in Hadoop. Additional Security Weaknesses. Hadoop Threat Model 3. Challenges and Threats in Hadoop Security. Hadoop Security Framework 3. Data Management. Threat Modeling. Getting and Installing Kerberos. Application Level Cryptography Tokenization, field-level encryption.
Network Security 3. Threat Model. Threat Model Development. Components of Hadoop 4. Working with files in HDFS 4. Basic file commands. Reading and writing to HDFS programmatically. Anatomy of a MapReduce program 4. Hadoop data types.
Word counting with predefined mapper and reducer classes. Reading and writing 4.
Writing basic MapReduce programs 5. Getting the patent data set 5. The patent citation data. Constructing the basic template of a MapReduce program 5. MapReduce v1 and v2. Streaming in Hadoop 5. Streaming with Unix commands. Streaming with the Aggregate package. Improving performance with combiners.
Exercising what you've learned. Programming practices 7. Developing MapReduce programs 7. Local mode. Pseudo-distributed or Single Node Cluster mode.
Monitoring and debugging on a production cluster 7. Rerunning failed tasks with IsolationRunner.
Tuning for performance 7. Reducing network traffic with combiner. Reducing the amount of input data. Running with speculative execution. Refactoring code and rewriting algorithms. Data Security for Data Management 8. HDFS Security.