Apache Hive vs Apache Kylin: What are the differences?

Introduction

In this article, we will discuss the key differences between Apache Hive and Apache Kylin. Both Hive and Kylin are widely used distributed query engines for big data processing, but they differ in several aspects. Let's explore these differences in detail.

1. Storage and Querying Approach: Apache Hive is primarily designed for batch processing and ad-hoc queries on large datasets stored in Hadoop Distributed File System (HDFS). It uses a traditional relational database approach with structured query language (SQL) for querying and supports various file formats. On the other hand, Apache Kylin is an online analytical processing (OLAP) engine that specifically focuses on providing sub-second query response time for multidimensional data analysis. It uses a pre-computed, cube-based approach to speed up query processing by aggregating data in advance.

2. Data Model: Hive follows a schema-on-read approach, where the schema is applied at the time of querying rather than during data ingestion. It allows flexibility in handling unstructured or semi-structured data, as the schema can be defined on the fly. Kylin, on the other hand, follows a schema-on-write approach, where the schema is defined during data ingestion itself. This allows for better query performance as the data is pre-aggregated and optimized for querying.

3. Query Performance: Hive queries may take longer to execute due to the batch processing nature and the need to scan large volumes of data. It is not suitable for real-time or interactive querying. Kylin, on the other hand, excels in query performance by leveraging pre-aggregated data cubes. It is designed to provide sub-second query response time, making it suitable for interactive data analysis and reporting.

4. Indexing: Hive uses traditional indexes like Bitmap indexes and B+ trees for query optimization. These indexes may not be sufficient for high-performance analytical queries on large datasets. Kylin, however, uses a combination of bitmap indexes, bitmap join indexes, and pre-computed data cubes to provide efficient indexing and aggregation capabilities. This allows it to achieve fast query performance on multidimensional data.

5. Data Storage: Hive stores data in HDFS or other file systems supported by Hadoop. It can work with a wide range of file formats and supports data compression techniques. Kylin, on the other hand, stores pre-computed data cubes in its own storage format optimized for high-performance query processing. This format is specifically designed for OLAP analysis and may not be suitable for general-purpose data storage.

6. Data Exploration and Visualization: Hive provides basic data exploration and visualization capabilities through tools like Apache Zeppelin or third-party BI tools that can connect to Hive using JDBC or ODBC drivers. Kylin, on the other hand, provides advanced data exploration and visualization capabilities through its web-based user interface. It offers features like data cube browsing, visual query building, and charting for interactive analysis.

In summary, Apache Hive and Apache Kylin differ in their storage and querying approach, data model, query performance, indexing techniques, data storage formats, and data exploration capabilities. Hive is suitable for batch processing and ad-hoc queries on large datasets, while Kylin excels in providing sub-second query response time for multidimensional data analysis.