Apache Calcite is a foundational software framework that provides query processing, optimization, and query language support to many popular open-source data processing systems such as Apache Hive, Apache Storm, Apache Flink, Druid, and MapD. The goal of this paper is to formally introduce Calcite to the broader research community, brie y present its history, and describe its architecture, features, functionality, and patterns for adoption. Calcite's architecture consists of a modular and extensible query optimizer with hundreds of built-in optimization rules, a query processor capable of processing a variety of query languages, an adapter architecture designed for extensibility, and support for heterogeneous data models and stores (relational, semi-structured, streaming, and geospatial). This exible, embeddable, and extensible architecture is what makes Calcite an attractive choice for adoption in big-data frameworks. It is an active project that continues to introduce support for the new types of data sources, query languages, and approaches to query processing and optimization.

https://dl.acm.org/doi/pdf/10.1145/3183713.3190662

Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources

Apache Hive is an open-source relational database system for analytic big-data workloads. In this paper we describe the key innovations on the journey from batch tool to fully fledged enterprise data warehousing system. We present a hybrid architecture that combines traditional MPP techniques with more recent big data and cloud concepts to achieve the scale and performance required by today's analytic applications. We explore the system by detailing enhancements along four main axis: Transactions, optimizer, runtime, and federation. We then provide experimental results to demonstrate the performance of the system for typical workloads and conclude with a look at the community roadmap.

pdf/apache-hive-from-mapreduce-to-enterprise-grade-big-data-1oj6hftma6.pdf

Apache Hive: From MapReduce to Enterprise-grade Big Data Warehousing

Apache Spark is an in-memory data processing system that supports both SQL queries and advanced analytics over large data sets. In this paper, we present our design and implementation of Spark-GPU that enables Spark to utilize GPU's massively parallel processing ability to achieve both high performance and high throughput. Spark-GPU transforms a general-purpose data processing system into a GPU-supported system by addressing several real-world technical challenges including minimizing internal and external data transfers, preparing a suitable data format and a batching mode for efficient GPU execution, and determining the suitability of workloads for GPU with a task scheduling capability between CPU and GPU. We have comprehensively evaluated Spark-GPU with a set of representative analytical workloads to show its effectiveness. Our results show that Spark-GPU improves the performance of machine learning workloads by up to 16.13x and the performance of SQL queries by up to 4.83x.

Spark-GPU: An accelerated in-memory data processing engine on clusters

Hadoop and Spark are widely used distributed processing frameworks for large-scale data processing in an efficient and fault-tolerant manner on private or public clouds. These big-data processing systems are extensively used by many industries, e.g., Google, Facebook, and Amazon, for solving a large class of problems, e.g., search, clustering, log analysis, different types of join operations, matrix multiplication, pattern matching, and social network analysis. However, all these popular systems have a major drawback in terms of locally distributed computations, which prevent them in implementing geographically distributed data processing. The increasing amount of geographically distributed massive data is pushing industries and academia to rethink the current big-data processing systems. The novel frameworks, which will be beyond state-of-the-art architectures and technologies involved in the current system, are expected to process geographically distributed data at their locations without moving entire raw datasets to a single location. In this paper, we investigate and discuss challenges and requirements in designing geographically distributed data processing frameworks and protocols. We classify and study batch processing (MapReduce-based systems), stream processing (Spark-based systems), and SQL-style processing geo-distributed frameworks, models, and algorithms with their overhead issues.

A Survey on Geographically Distributed Big-Data Processing using MapReduce

Industrial systems, e.g., wind turbines, generate big amounts of data from reliable sensors with high velocity. As it is unfeasible to store and query such big amounts of data, only simple aggregates are currently stored. However, aggregates remove fluctuations and outliers that can reveal underlying problems and limit the knowledge to be gained from historical data. As a remedy, we present the distributed Time Series Management System (TSMS) ModelarDB that uses models to store sensor data. We thus propose an online, adaptive multi-model compression algorithm that maintains data values within a user-defined error bound (possibly zero). We also propose (i) a database schema to store time series as models, (ii) methods to push-down predicates to a key-value store utilizing this schema, (iii) optimized methods to execute aggregate queries on models, (iv) a method to optimize execution of projections through static code-generation, and (v) dynamic extensibility that allows new models to be used without recompiling the TSMS. Further, we present a general modular distributed TSMS architecture and its implementation, ModelarDB, as a portable library, using Apache Spark for query processing and Apache Cassandra for storage. An experimental evaluation shows that, unlike current systems, ModelarDB hits a sweet spot and offers fast ingestion, good compression, and fast, scalable online aggregate query processing at the same time. This is achieved by dynamically adapting to data sets using multiple models. The system degrades gracefully as more outliers occur and the actual errors are much lower than the bounds.

/pdf/modelardb-modular-model-based-time-series-management-with-46jzmahssh.pdf

ModelarDB: modular model-based time series management with spark and cassandra

Apache Hive is a widely used data warehouse system for Apache Hadoop, and has been adopted by many organizations for various big data analytics applications. Closely working with many users and organizations, we have identified several shortcomings of Hive in its file formats, query planning, and query execution, which are key factors determining the performance of Hive. In order to make Hive continuously satisfy the requests and requirements of processing increasingly high volumes data in a scalable and efficient way, we have set two goals related to storage and runtime performance in our efforts on advancing Hive. First, we aim to maximize the effective storage capacity and to accelerate data accesses to the data warehouse by updating the existing file formats. Second, we aim to significantly improve cluster resource utilization and runtime performance of Hive by developing a highly optimized query planner and a highly efficient query execution engine. In this paper, we present a community-based effort on technical advancements in Hive. Our performance evaluation shows that these advancements provide significant improvements on storage efficiency and query execution performance. This paper also shows how academic research lays a foundation for Hive to improve its daily operations.

/pdf/major-technical-advancements-in-apache-hive-5g652j6h1l.pdf

Major technical advancements in apache hive

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Major technical advancements in apache hive