Hive vs Pig

Between the dawn of civilization and 2003, there were about 5 EB (exabytes, 10^18 bytes) of information generated by the entire world. However, in today’s world, the same amount of information is created in every two days. But the most impressive thing is the capability of humankind to store, process, and analysis such incredibly huge bulks of data in reasonable time, simply by using frameworks like Hadoop.

When working with Hadoop, you will eventually come across Hive and Pig. Both are Apache’s products that are used to analyze big data sets without having to use the MapReduce code. Ultimately, Hive and Pig will produce the same results. However, the two go through different processes. Thus, there have been many people who get confused whether to use Hive or Pig – and why there are many cases in which both Hive and Pig are implemented together. Below, we will take a brief look at the overview of big data and Hadoop, and then we will see the differences between Hive and Pig.

What are Big Data? What is Hadoop?

In general, data can be categorized into one of three types: structured data, semi-structured data, and unstructured data. Structured data are those that can be completely stored in databases, such as the transaction records of an online purchase. Semi-structured data are those that can be partially stored in databases; for example, the data inside XML records can be partially stored in databases. Finally, any other form of data that cannot be classified as structured or semi-structured data falls into the category of unstructured data. The data from social networking websites and various web logs that cannot be analyzed or stored in databases are good examples.

“Big data” is the term that is often used to refer to unstructured data. Meanwhile, Hadoop is an open-source software framework made by Apache that is widely used for distributed storage and processing big data.

Overview of Apache Hive and Apache Pig

Apache Hive is data warehouse software that is built on top of Hadoop, used for data summarization, query, and analysis. Apache Hive features an SQL-like interface to query the data stored in various databases as well as file systems integrated with Hadoop. While traditional SQL queries need to be implemented in order to execute SQL applications/queries over distributed data, Hive can provide the necessary SQL abstraction to integrate SQL-like queries (conveniently called HiveQL) into the underlying Java without requiring you to implement queries in the low-level API. Thus, Hive can help in the portability of SQL-based applications to Hadoop, since most data warehouse applications use SQL-based query languages. Even though Hive was originally developed by Facebook, other companies have now been using it, such as Netflix, Amazon, and the Financial Industry Regulatory Authority (FINRA).

On the other hand, Apache Pig is a high-level program creation platform that runs on top of Hadoop. Hence, it has its own language, which is called Pig Latin. Pig Latin abstracts the programming from the Java MapReduce idiom into a high-level notation. Pig Latin can be extended by using user-defined functions (UDF) in Java, JavaScript, Python, Ruby, or Groovy. Apache Pig is able to execute its Hadoop jobs in MapReduce, Apache Tez, as well as Apache Spark.

Hive vs. Pig: Usage and Function

As you can see, Hive and Pig are quite different by nature. Hive is primarily used for creating reports, and thus is mainly used by data analysts. However, since it is heavily SQL-like, Hive can only support structured data.

On the other hand, Pig is primarily used for programming and is used by both researchers and programmers. Pig is able to process structured data as well as unstructured data. Since it is a program creation platform, it is generally more flexible and convenient for constructing data pipelines than Hive. This is because of several reasons:
– Pig Latin is procedural, while SQL is declarative;
– Pig Latin enables you to choose where to checkpoint data in the pipeline;
– Pig Latin enables you to select operator implementations directly instead of relying on the optimizer;
– Pig Latin enables you to insert your own code almost anywhere in the pipeline;
– Pig Latin supports splits in the data pipeline.

Hive vs. Pig: Language

Hive works with a declarative SQL-like language, and it uses an exact variation of the SQL DDL language, defining tables beforehand. It stores the schema details in any local database. Since it directly leverages SQL, it is much easier to learn, especially by those already familiar with databases.

Actually, Hive also supports user defined functions (UDF). But UDF will be much harder to debug here.

On the other hand, Pig works with its own language, which is called Pig Latin. Pig Latin is a procedural data flow language. Even though Pig Latin has some similarities to SQL, the language varies to a great extent. Thus, it can be more difficult to learn. Pig Latin does not have any dedicated metadata database; hence, the schema details are defined in the script.

It is very easy to write and debug UDF in Pig. UDF in Pig is especially very useful for calculating matrices.

Hive vs. Pig: Operation

In the cluster, Hive operates on the server side. Before you can run Hive, you have to start Hadoop first. Meanwhile, Pig always operates on the client side of a cluster, can run in either the standalone mode or the cluster mode, and does not require you to start Hadoop in order for it to run (although you still have to install Hadoop first).

Hive does not support the Avro file format. Hive executes quickly, but does not load quickly. Pig supports the Avro file format, and loads data quickly and effectively.

HivePig
- Uses a declarative SQL-like language- Uses Pig Latin, a procedural data flow language
- Directly leverages SQL, easier to learn especially by those familiar with databases- Has some similarities to SQL but also varies to a great extent
- Stores schema details in any local database, defining tables beforehand- Does not have a dedicated metadata database; defines schema details in the script
- Mainly used for creating reports by data analysts- Mainly used for programming by researchers and programmers
- Very difficult to debug UDF, does not support Avro- Much easier to write and debug UDF, supports Avro
- Runs on the server side of a cluster, requires Hadoop to start first- Runs on the client side of a cluster, does not require Hadoop to start first
- Executes quickly but doesn’t load quickly; less efficient for constructing data pipelines- Loads quickly and efficiently; more efficient for constructing data pipelines

Conclusion

As you can see, Hive and Pig are quite different by nature. Hive is heavily SQL-like, with a declarative language, and thus can only support structured data. Hive is mainly used by data analysts for creating reports, and is generally easier to learn. On the other hand, Pig has its own language, Pig Latin, which is a procedural language. Pig supports structured and unstructured data. Pig is mainly used for programming and is usually used by researchers as well as programmers. Pig is relatively more difficult to learn.

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