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WHY IS SPARK FOR MAC SO SLOW HOW TO

The data is read into a Spark DataFrame or, DataSet or RDD (Resilient Distributed Dataset). These APIs would use a definite number of partitions which are mapped to one of more input data files, and the mapping is done either on a part of the file or entire file. The advantage of using Azure Databricks for data loading is that Spark engine reads the input file in parallel through dedicated Spark APIs. Azure Data Factory or another spark engine-based platform. To achieve maximum concurrency and high throughput for writing to SQL table and reading a file from ADLS (Azure Data Lake Storage) Gen 2, Azure Databricks was chosen as a choice of platform, although we have other options to choose from, viz. Remember, BULK INSERT is a single threaded operation and hence one single stream would read and write it to the table, thus reducing load throughput. In the first test, a single BULK INSERT was used to load data into Azure SQL Database table with Clustered Columnstore Index and no surprises here, it took more than 30 minutes to complete, depending on the BATCHSIZE used. This is a custom data set with random data.Ī typical high-level architecture of Bulk ingestion or ingestion post-transformation (ELT\ETL) would look similar to the one given below: The CSV file size is 27 GB having 110 M records with 36 columns. In this test, the data was loaded from a CSV file located on Azure Data Lake Storage Gen 2. Pre-requisite: Before going further through this article spend some time to understand Overview of Loading Data into Columnstore Indexes here: Data Loading performance considerations with Clustered Columnstore indexes Standard_DS3_v2 14.0 GB Memory, 4 Cores, 0.75 DBU (8 Worker Nodes Max).Azure Databricks – 6.6 (includes Apache Spark 2.4.5, Scala 2.11).Azure SQL Database – Business Critical, Gen5 80vCores.The input data set have one file with columns of type int, nvarchar, datetime etc. One CSV file of 27 GB, 110 M records with 36 columns. Custom curated data set – for one table only.

* Note: There could be some serious implications of parallel inserting data into Clustered Index as mentioned in Guidelines for Optimizing Bulk Import and The Data Loading Performance Guide These guidelines and explanations are still valid for the latest versions of SQL Server. Essentially, improving row group quality is an important factor for determining query performance. The most interesting observation shared in this article is to exhibit how the Clustered Columnstore Index row group quality is degraded when default spark configurations are used, and how the quality can be improved by efficient use of spark partitioning.

WHY IS SPARK FOR MAC SO SLOW HOW TO

This article is to showcase how to take advantage of a highly distributed framework provided by spark engine by carefully partitioning the data before loading into a Clustered Columnstore Index of a relational database like SQL Server or Azure SQL Database. The destination could be a Heap, Clustered Index * or Clustered Columnstore Index.

In this article, we have used Azure Databricks spark engine to insert data into SQL Server in parallel stream (multiple threads loading data into a table) using a single input file. In such scenarios utilizing Apache Spark engine is one of the popular methods of loading bulk data to SQL tables concurrently.

The size of file(s) to be loaded spans through several GBs (say more than 20 GB and above), each containing millions of records.

Every incoming data file is of different size, which makes it difficult to identify the number of chunks (to split the file into) and dynamically define BULK INSERT statements to execute for each chunk.

Splitting a file isn’t an option as it will be an extra step in the overall bulk load operation.

Load data from a single file of a large size (say, more than 20 GB).

However, for concurrent loads you may insert into the same table using multiple BULK INSERT statements, provided there are multiple files to be read.Ĭonsider a scenario where the requirements are: Reviewers: Arvind Shyamsundar, Denzil Ribeiro, Davide Mauri, Mohammad Kabiruddin, Mukesh Kumar, and Narendra Anganeīulk load methods on SQL Server are by default serial, which means for example, one BULK INSERT statement would spawn only one thread to insert the data into a table.

Authors: Sumit Sarabhai and Ravinder Singh