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How to Use Snappy Compression with Hive and Hadoop (Updated)

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Snappy is a fast compression codec widely used in Hadoop ecosystems. This updated guide shows how to generate data, upload it to HDFS, process it with Hive using Snappy compression, verify the output files, and load Snappy-compressed data back into Hive using modern Hadoop commands.

Snappy is a high-performance compression and decompression library originally developed at Google. It is optimized for speed rather than maximum compression ratio, making it a preferred codec in many Hadoop and Hive pipelines.

Snappy is integrated in all modern Hadoop distributions (Hadoop 2.x and 3.x) and works with Hive, MapReduce, and increasingly Tez or Spark-based Hive deployments.

1. Create sample input data

Generate a small test file:

$ seq 1 1000 | awk '{OFS="\001";print $1, $1 % 10}' > test_input.hive
$ head -5 test_input.hive
1^A1
2^A2
3^A3
4^A4
5^A5

2. Upload the data into HDFS

Updated command: Hadoop now uses hdfs dfs instead of hadoop dfs.

$ hdfs dfs -mkdir -p /tmp/hivetest
$ hdfs dfs -put test_input.hive /tmp/hivetest

$ hdfs dfs -ls /tmp/hivetest
Found 1 items
-rw-r--r--   3 hdfs supergroup   5893 ... /tmp/hivetest/test_input.hive

3. Create an external Hive table

External tables in Hive reference a directory:

hive> CREATE EXTERNAL TABLE hivetest1 (a INT, b INT)
      ROW FORMAT DELIMITED FIELDS TERMINATED BY '\001'
      LOCATION '/tmp/hivetest';

hive> SELECT * FROM hivetest1 LIMIT 1;
1     1

4. Enable Snappy in Hive

Before writing compressed output, enable Snappy via session settings:

hive> SET hive.exec.compress.output=true;
hive> SET mapreduce.output.fileoutputformat.compress.codec=org.apache.hadoop.io.compress.SnappyCodec;
hive> SET mapreduce.output.fileoutputformat.compress.type=BLOCK;

Note: In Hadoop 2.x and 3.x, the modern property names start with mapreduce.output. instead of mapred.output.

Now create a managed table for compressed output: 

hive> CREATE TABLE hivetest2 (a INT, b INT);

5. Insert data into the Snappy-compressed table

Hive will launch a MapReduce (or Tez) job that produces Snappy-compressed output blocks: 

hive> INSERT OVERWRITE TABLE hivetest2
      SELECT * FROM hivetest1;

Total jobs = 1
... job output omitted ...
1000 Rows loaded to hivetest2

6. Validate the compressed data file

Verify that Hive created a Snappy file:

$ hdfs dfs -ls /user/hive/warehouse/hivetest2
Found 1 items
-rw-r--r--   3 hdfs supergroup  4021 ... /user/hive/warehouse/hivetest2/000000_0.snappy

Read the Snappy file (it will appear as binary with readable records):

$ hdfs dfs -cat /user/hive/warehouse/hivetest2/000000_0.snappy | head -5
...binary header...
1^A1
2^A2
3^A3
4^A4
5^A5

7. Load Snappy data into another Hive table

You can also load existing Snappy files using LOAD DATA:

hive> CREATE TABLE hivetest3 (a INT, b INT);

hive> LOAD DATA INPATH '/user/hive/warehouse/hivetest2/000000_0.snappy'
      INTO TABLE hivetest3;

hive> SELECT * FROM hivetest3 LIMIT 5;
1     1
2     2
3     3
4     4
5     5

Notes for Modern Hadoop Versions

  • Snappy is enabled by default in Hadoop 2.x+ as long as native libraries are installed.
  • On Hive-on-Tez or Spark, compression is still controlled via SET statements or table properties.
  • Modern Hive uses ORC or Parquet, which internally use Snappy by default.
  • This example remains valid for teaching raw file-based compression workflows.

Snappy remains a preferred codec due to its balance between speed and reasonable compression ratio. Always ensure that your Hadoop installation has native Snappy libraries available; otherwise, Hadoop will silently fall back to slow pure-Java implementations.

Related guides:

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