Compression codecs (Snappy, LZO, Gzip) in Hadoop - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main purpose of compression codecs like Snappy, LZO, and Gzip in Hadoop?

Compression codecs reduce the size of data stored or transferred, saving storage space and speeding up data processing by reducing I/O time.

beginner

Which compression codec among Snappy, LZO, and Gzip is known for the fastest compression and decompression speed?

Snappy is known for very fast compression and decompression speeds, making it suitable for real-time data processing.

intermediate

How does Gzip compression compare to Snappy and LZO in terms of compression ratio and speed?

Gzip offers a higher compression ratio (smaller files) but is slower in compression and decompression compared to Snappy and LZO.

intermediate

What is a key limitation of LZO compression in Hadoop?

LZO requires an index file to enable splitting of compressed files for parallel processing, which adds setup complexity.

beginner

Why might you choose Snappy over Gzip for compressing Hadoop data?

Choose Snappy when you need faster data processing and can accept a lower compression ratio, as it speeds up reading and writing data.

Which compression codec is fastest for decompression in Hadoop?

A Snappy

B Gzip

C LZO

D None of the above

Snappy is designed for very fast compression and decompression speeds.

Which codec typically produces the smallest compressed files?

A Snappy

B LZO

C Gzip

D All produce the same size

Gzip compresses data more tightly, resulting in smaller files but slower speed.

What extra file does LZO require for Hadoop to split compressed files?

A Index file

B Metadata file

C Config file

D No extra file needed

LZO needs an index file to allow Hadoop to split files for parallel processing.

If you want to speed up Hadoop data processing with compression, which codec is best?

A Gzip

B Snappy

C LZO

D No compression

Snappy offers fast compression and decompression, improving processing speed.

Which codec is slower but compresses data more tightly?

A None

B LZO

C Snappy

D Gzip

Gzip compresses data more but is slower than Snappy and LZO.

Explain the trade-offs between Snappy, LZO, and Gzip compression codecs in Hadoop.

Think about speed versus file size and Hadoop processing needs.

Describe why compression codecs are important in Hadoop data processing.

Consider how big data systems handle lots of data.

Practice

1. Which compression codec is known for the fastest compression and decompression speed in Hadoop?

easy

A. LZO

B. Gzip

C. Snappy

D. Bzip2

Solution

1. Step 1: Understand codec speed characteristics

   Snappy is designed for speed, offering fast compression and decompression.

2. Step 2: Compare with other codecs

   Gzip compresses better but slower; LZO is fast but usually slower than Snappy; Bzip2 is slowest.

3. Final Answer:

   Snappy -> Option C

4. Quick Check:

   Fastest codec = Snappy [OK]

Hint: Snappy is fastest codec for speed, not best compression [OK]

Common Mistakes:

• Confusing Gzip as fastest due to better compression
• Thinking LZO is faster than Snappy
• Assuming Bzip2 is fast

2. Which of the following is the correct way to enable Snappy compression in a Hadoop job configuration XML?

easy

A. mapreduce.output.fileoutputformat.compress.codecorg.apache.hadoop.io.compress.GzipCodec

B. mapreduce.output.fileoutputformat.compress.codecorg.apache.hadoop.io.compress.SnappyCodec

C. mapreduce.output.fileoutputformat.compress.codecorg.apache.hadoop.io.compress.Lz4Codec

D. mapreduce.output.fileoutputformat.compress.codecorg.apache.hadoop.io.compress.Bzip2Codec

Solution

1. Step 1: Identify the property for compression codec

   The property key is 'mapreduce.output.fileoutputformat.compress.codec' to set codec class.

2. Step 2: Match the codec class for Snappy

   Snappy codec class is 'org.apache.hadoop.io.compress.SnappyCodec'.

3. Final Answer:

   Property with SnappyCodec class -> Option B

4. Quick Check:

   Snappy codec class = org.apache.hadoop.io.compress.SnappyCodec [OK]

Hint: Snappy codec class ends with SnappyCodec [OK]

Common Mistakes:

• Using GzipCodec or other codec classes by mistake
• Misspelling the property name
• Confusing Lz4Codec with LZO

3. Given the following Hadoop job configuration snippet, what compression codec will be used?

conf.set("mapreduce.output.fileoutputformat.compress", "true");
conf.set("mapreduce.output.fileoutputformat.compress.codec", "org.apache.hadoop.io.compress.GzipCodec");

medium

A. Snappy

B. LZO

C. No compression

D. Gzip

Solution

1. Step 1: Check if compression is enabled

   Property 'mapreduce.output.fileoutputformat.compress' is set to 'true', so compression is enabled.

2. Step 2: Identify the codec class used

   Codec class is 'org.apache.hadoop.io.compress.GzipCodec', which means Gzip compression is used.

3. Final Answer:

   Gzip -> Option D

4. Quick Check:

   Compression enabled with Gzip codec [OK]

Hint: Check compress property true + codec class name [OK]

Common Mistakes:

• Ignoring the compress flag and assuming no compression
• Confusing codec class with Snappy or LZO
• Assuming default codec if not checked

4. You configured LZO compression in your Hadoop job, but the job fails with a codec not found error. What is the most likely cause?

medium

A. LZO native libraries are not installed or not in the library path

B. The job configuration property name is misspelled

C. Snappy codec is enabled instead of LZO

D. Gzip compression is incompatible with Hadoop

Solution

1. Step 1: Understand LZO codec requirements

   LZO requires native libraries installed on all cluster nodes to work properly.

2. Step 2: Analyze error cause

   Codec not found error usually means native LZO libraries are missing or not accessible in the library path.

3. Final Answer:

   LZO native libraries missing or not in path -> Option A

4. Quick Check:

   LZO needs native libs installed [OK]

Hint: LZO needs native libs installed on cluster nodes [OK]

Common Mistakes:

• Assuming misspelled property causes codec not found
• Confusing Snappy and LZO codec errors
• Thinking Gzip is incompatible with Hadoop

5. You want to compress large Hadoop output files with the best compression ratio but can tolerate slower speed. Which codec should you choose and why?

hard

A. Gzip, because it offers the best compression ratio despite slower speed

B. LZO, because it balances speed and compression

C. Snappy, because it is fastest and good compression

D. No compression, to avoid overhead

Solution

1. Step 1: Identify compression needs

   Best compression ratio means smallest file size, speed is less important.

2. Step 2: Compare codecs by compression ratio and speed

   Gzip compresses better but slower; Snappy is fastest but less compression; LZO is in between.

3. Step 3: Choose codec matching needs

   Gzip fits best for high compression ratio with slower speed tolerance.

4. Final Answer:

   Gzip, best compression ratio with slower speed -> Option A

5. Quick Check:

   Best compression ratio = Gzip [OK]

Hint: Best compression ratio usually means Gzip codec [OK]

Common Mistakes:

• Choosing Snappy for best compression ratio
• Thinking LZO compresses better than Gzip
• Avoiding compression despite need to save space