HTAP With Reactive Streaming ETL

Vitezslav Nezval,Joseph G Vella,Carl Camilleri

doi:10.4018/jcit.20211001.oa10

Vitezslav Nezval, Joseph G Vella + Show 1 more

Open Access

https://doi.org/10.4018/jcit.20211001.oa10

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Abstract

In database management systems (DBMSs), query workloads can be classified as online transactional processing (OLTP) or online analytical processing (OLAP). These often run within separate DBMSs. In hybrid transactional and analytical processing (HTAP), both workloads may execute within the same DBMS. This article shows that it is possible to run separate OLTP and OLAP DBMSs, and still support timely business decisions from analytical queries running off fresh transactional data. Several setups to manage OLTP and OLAP workloads are analysed. Then, benchmarks on two industry standard DBMSs empirically show that, under an OLTP workload, a row-store DBMS sustains a 1000 times higher throughput than a columnar DBMS, whilst OLAP queries are more than 4 times faster on a columnar DBMS. Finally, a reactive streaming ETL pipeline is implemented which connects these two DBMSs. Separate benchmarks show that OLTP events can be streamed to an OLAP database within a few seconds.

Highlights

In database management systems (DBMS), query workloads are segmented into two broad modes (Elnaffar et al, 2002; Li et al, 2019)
In database management systems (DBMSs), query workloads can be classified as online transactional processing (OLTP) or online analytical processing (OLAP)
OLTP and OLAP databases were deployed as PostgreSQL and Vertica, in the same configuration detailed previously

Summary

Introduction

In database management systems (DBMS), query workloads are segmented into two broad modes (Elnaffar et al, 2002; Li et al, 2019). Online transactional processing (OLTP) workloads typically consist of write queries that modify small amounts of data, and queries that read a few records whilst projecting the majority of the attributes available (Bach & Werner, 2016). At the other end of the spectrum, Online analytical processing (OLAP) workloads typically consist of read-only queries which traverse a large amount of records, performing aggregations and projecting a narrow set of attributes (Bach & Werner, 2016). Attributes, of a tuple stored in a page are serialised sequentially This assertion holds in general for values of simple data types, specific approaches are taken for values of types which are larger than the size of a page

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