Open Source Multidimensional Table Selection Guide

This article discusses the factors that affect the performance of open-source multidimensional table stores for large datasets, the key features to consider when choosing a store, and the scalability and extensibility of different stores. It provides

Which open-source multidimensional table store has the best performance for large datasets?

The performance of an open-source multidimensional table store for large datasets depends on several factors, including the specific implementation, the hardware it runs on, and the size and complexity of the dataset. However, some general guidelines can help you choose a high-performance store.

• Look for a store that uses a column-oriented storage model. Column-oriented stores are more efficient for storing and querying large datasets than row-oriented stores, because they can access data in columns without having to read the entire row.
• Choose a store that supports parallel processing. Parallel processing can significantly improve the performance of large-dataset queries by distributing the workload across multiple processors.
• Consider the size of your dataset and the frequency of your queries. If you have a very large dataset and you need to perform frequent queries, you may need a store that supports distributed storage. Distributed storage can help to reduce the latency of queries by spreading the data across multiple servers.

What are the key features to consider when choosing an open-source multidimensional table store for a specific application?

When choosing an open-source multidimensional table store for a specific application, you should consider the following key features:

• Data model: The data model of a store determines the types of data that it can store and the operations that can be performed on the data. Choose a store that supports a data model that is appropriate for your application.
• Query language: The query language of a store determines the types of queries that can be performed on the data. Choose a store that supports a query language that is expressive enough for your application.
• Performance: The performance of a store is important for applications that require fast data access. Consider the factors discussed in the previous question when evaluating the performance of a store.
• Scalability: The scalability of a store determines how well it can handle increasing data volumes and query loads. Choose a store that is scalable enough for your application.
• Extensibility: The extensibility of a store determines how easy it is to add new features and functionality. Choose a store that is extensible enough to meet your future needs.

How do different open-source multidimensional table stores compare in terms of scalability and extensibility?

Different open-source multidimensional table stores offer different levels of scalability and extensibility. Some stores are designed to handle large datasets and high query loads, while others are more suitable for smaller applications. Some stores are also more extensible than others, making them easier to customize for specific needs.

The following table compares the scalability and extensibility of several popular open-source multidimensional table stores:

Store	Scalability	Extensibility
Apache Druid	High	High
Apache Kylin	High	Medium
Apache Pinot	High	High
Druid	High	High
HBase	High	Low
Impala	Medium	Low
Presto	Medium	Low
Spark SQL	Medium	High

As you can see, Apache Druid, Apache Pinot, and Druid are the most scalable and extensible open-source multidimensional table stores. HBase and Impala are also scalable, but they are less extensible. Presto and Spark SQL are less scalable and extensible than the other stores.

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