Compression codecs (Snappy, LZO, Gzip) in Hadoop - Step-by-Step Execution

Concept Flow - Compression codecs (Snappy, LZO, Gzip)

Input Data

↓

Choose Codec: Snappy / LZO / Gzip

↓

Compress Data

↓

Store Compressed Data

↓

Read Compressed Data

↓

Decompress Data

↓

Output Original Data

Data flows from input through a chosen compression codec, then stored compressed, later read and decompressed back to original.

Execution Sample

Hadoop

input_data = 'Example data to compress'
codec = 'Snappy'
compressed = compress(input_data, codec)
decompressed = decompress(compressed, codec)
print(decompressed)

This code compresses a string using Snappy codec and then decompresses it back to original.

Execution Table

Step	Action	Input	Codec	Output	Notes
1	Start with input data	'Example data to compress'	-	'Example data to compress'	Initial raw data
2	Select codec	-	Snappy	-	Snappy chosen for compression
3	Compress data	'Example data to compress'	Snappy	b'compressed_bytes_snappy'	Data compressed using Snappy
4	Store compressed data	b'compressed_bytes_snappy'	-	Stored compressed data	Data saved in compressed form
5	Read compressed data	Stored compressed data	-	b'compressed_bytes_snappy'	Compressed data loaded
6	Decompress data	b'compressed_bytes_snappy'	Snappy	'Example data to compress'	Data decompressed back to original
7	Output data	'Example data to compress'	-	'Example data to compress'	Final output matches input

💡 Process ends after decompression returns original data

Variable Tracker

Variable	Start	After Compression	After Storage	After Read	After Decompression	Final
input_data	'Example data to compress'	'Example data to compress'	'Example data to compress'	'Example data to compress'	'Example data to compress'	'Example data to compress'
codec	-	Snappy	Snappy	Snappy	Snappy	Snappy
compressed	-	b'compressed_bytes_snappy'	b'compressed_bytes_snappy'	b'compressed_bytes_snappy'	-	-
decompressed	-	-	-	-	'Example data to compress'	'Example data to compress'

Key Moments - 3 Insights

Why does the compressed data look like unreadable bytes?

Compressed data is binary and optimized for size, not readability. See step 3 in execution_table where output is shown as bytes.

Can we decompress data with a different codec than used for compression?

No, decompression must use the same codec as compression to restore original data. See step 6 where Snappy is used again.

Why do we store compressed data instead of original?

Storing compressed data saves space and speeds up data transfer. See step 4 where compressed data is stored.

Visual Quiz - 3 Questions

Look at the execution_table, what is the output after step 3 (Compress data)?

A Compressed binary bytes

B Original readable string

C Decompressed string

D Empty data

💡 Hint

Check the Output column at step 3 in execution_table

At which step does the data get restored to its original form?

A Step 3

B Step 5

C Step 6

D Step 4

💡 Hint

Look for decompression action and output matching original data in execution_table

If we change codec from Snappy to Gzip, what changes in the execution_table?

A Decompressed data changes

B Only the codec column changes, compressed output changes accordingly

C Input data changes

D No changes at all

💡 Hint

Codec choice affects compression and decompression steps, see codec column in execution_table

Concept Snapshot

Compression codecs reduce data size for storage and transfer.
Common codecs: Snappy (fast), LZO (fast), Gzip (high compression).
Process: input data → compress → store → read → decompress → original data.
Use same codec for compress and decompress.
Compressed data is binary and not human-readable.

Full Transcript

Compression codecs like Snappy, LZO, and Gzip help reduce data size in Hadoop. The process starts with input data, which is compressed using a chosen codec. The compressed data is stored and later read back. To get the original data, the compressed data is decompressed using the same codec. Compressed data appears as unreadable bytes because it is binary. Using the wrong codec for decompression will fail to restore the original data. This process saves storage space and speeds up data transfer.

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