The impact of columnar file formats on SQL‐on‐hadoop engine performance: A study on ORC and Parquet

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Benchmarking BigSQL Systems

Elame suurandmete ajastul. Tänapäeval on olemas suurandmete töötlemise süsteemid, mis on võimelised haldama sadu terabaite ja petabaite andmeid. Need süsteemid töötlevad andmehulki, mis on liiga suured traditsiooniliste andmebaasisüsteemide jaoks. Mõned neist süsteemidest sisaldavad SQL keeli andmehoidlaga suhtlemiseks. Nendel süsteemidel, mida nimetatakse ka BigSQL süsteemideks, on mõned omadused, mis teevad nende andmete hoidmist ja haldamist unikaalseks. Süsteemide paremaks mõistmiseks on vajalik nende jõudluse ja omaduste uuring. Antud töö sisaldab BigSQL süsteemide jõudluse võrdlusuuringut. Valitud BigSQL süsteemdiega viiakse läbi standardiseeritud jõudlustestid ja eksperimentidest saadud tulemusi analüüsitakse. Töö eesmärgiks on seletada paremini lahti valitud BigSQL süsteemide omadusi ja käitumist.We live in the era of BigData. We now have BigData systems which are able to manage data in volumes of hundreds of terabytes and petabytes. These BigData systems handle data sizes which are too large for traditional database systems to handle. Some of these BigData systems now provide SQL syntax for interacting with their store. These BigData systems, referred to as BigSQL systems, possess certain features which make them unique in how they manage the stored. A study into the performances and characteristics of these BigSQL systems is necessary in order to better understand these systems. This thesis provides that study into the performance of these BigSQL systems. In this thesis, we perform standardized benchmark experiments against some selected BigSQL systems and then analyze the performances of these systems based on the results of the experiments. The output of this thesis study will provide an understanding of the features and behaviors of the BigSQL systems

Characterizing BigBench Queries, Hive, and Spark in Multi-cloud Environments

BigBench is the new standard (TPCx-BB) for benchmarking and testing Big Data systems. The TPCx-BB specification describes several business use cases—queries—which require a broad combination of data extraction techniques including SQL, Map/Reduce (M/R), user code (UDF), and Machine Learning to fulfill them. However, currently, there is no widespread knowledge of the different resource requirements and expected performance of each query, as is the case to more established benchmarks. Moreover, over the last year, the Spark framework and APIs have been evolving very rapidly, with major improvements in performance and the stable release of v2. It is our intent to compare the current state of Spark to Hive’s base implementation which can use the legacy M/R engine and Mahout or the current Tez and MLlib frameworks. At the same time, cloud providers currently offer convenient on-demand managed big data clusters (PaaS) with a pay-as-you-go model. In PaaS, analytical engines such as Hive and Spark come ready to use, with a general-purpose configuration and upgrade management. The study characterizes both the BigBench queries and the out-of-the-box performance of Spark and Hive versions in the cloud. At the same time, comparing popular PaaS offerings in terms of reliability, data scalability (1 GB to 10 TB), versions, and settings from Azure HDinsight, Amazon Web Services EMR, and Google Cloud Dataproc. The query characterization highlights the similarities and differences in Hive an Spark frameworks, and which queries are the most resource consuming according to CPU, memory, and I/O. Scalability results show how there is a need for configuration tuning in most cloud providers as data scale grows, especially with Sparks memory usage. These results can help practitioners to quickly test systems by picking a subset of the queries which stresses each of the categories. At the same time, results show how Hive and Spark compare and what performance can be expected of each in PaaS.This project has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement No. 639595). It is also partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya under contract 2014SGR1051, by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program (SEV-2015-0493).Peer Reviewe

Characterizing BigBench Queries, Hive, and Spark in Multi-cloud Environments

BigBench is the new standard (TPCx-BB) for benchmarking and testing Big Data systems. The TPCx-BB specification describes several business use cases—queries—which require a broad combination of data extraction techniques including SQL, Map/Reduce (M/R), user code (UDF), and Machine Learning to fulfill them. However, currently, there is no widespread knowledge of the different resource requirements and expected performance of each query, as is the case to more established benchmarks. Moreover, over the last year, the Spark framework and APIs have been evolving very rapidly, with major improvements in performance and the stable release of v2. It is our intent to compare the current state of Spark to Hive’s base implementation which can use the legacy M/R engine and Mahout or the current Tez and MLlib frameworks. At the same time, cloud providers currently offer convenient on-demand managed big data clusters (PaaS) with a pay-as-you-go model. In PaaS, analytical engines such as Hive and Spark come ready to use, with a general-purpose configuration and upgrade management. The study characterizes both the BigBench queries and the out-of-the-box performance of Spark and Hive versions in the cloud. At the same time, comparing popular PaaS offerings in terms of reliability, data scalability (1 GB to 10 TB), versions, and settings from Azure HDinsight, Amazon Web Services EMR, and Google Cloud Dataproc. The query characterization highlights the similarities and differences in Hive an Spark frameworks, and which queries are the most resource consuming according to CPU, memory, and I/O. Scalability results show how there is a need for configuration tuning in most cloud providers as data scale grows, especially with Sparks memory usage. These results can help practitioners to quickly test systems by picking a subset of the queries which stresses each of the categories. At the same time, results show how Hive and Spark compare and what performance can be expected of each in PaaS.This project has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement No. 639595). It is also partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya under contract 2014SGR1051, by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program (SEV-2015-0493).Peer Reviewe