{"id":2208,"date":"2016-03-29T15:00:00","date_gmt":"2016-03-29T15:00:00","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=2208"},"modified":"2023-06-23T05:20:40","modified_gmt":"2023-06-23T12:20:40","slug":"load-csv-data-into-couchbase-using-apache-spark","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/load-csv-data-into-couchbase-using-apache-spark\/","title":{"rendered":"Load CSV Data into Couchbase using Apache Spark"},"content":{"rendered":"<p>I&#8217;ve been spending a lot of time working with Big Data tools lately, in particular Apache Spark. In case you&#8217;re unfamiliar, Apache<br \/>\nSpark is an incredibly efficient tool for processing massive amounts of data. It performs significantly better than MapReduce, and in<br \/>\nreality, it isn&#8217;t too difficult to use.<\/p>\n<p>Apache Spark works very will in combination with Couchbase through the Couchbase Spark Connector. We&#8217;re going to see what it takes to<br \/>\nload some raw comma separated value (CSV) data into Couchbase using Apache Spark.<\/p>\n<h2>The Requirements<\/h2>\n<p>There are not too many requirements to get this project up and running. At a mimimum you&#8217;ll need the following:<\/p>\n<ul>\n<li><a href=\"https:\/\/spark.apache.org\/downloads.html\">Apache Spark<\/a> 1.6.1<\/li>\n<li>JDK 1.8+<\/li>\n<li>Apache Maven 3.3+<\/li>\n<li><a href=\"https:\/\/www.couchbase.com\/downloads\/\">Couchbase Server<\/a> 4.1+<\/li>\n<\/ul>\n<p>Most of the development will happen with the JDK 1.8 and Maven, but when it comes time to running the application, Apache Spark will<br \/>\nbe needed, whether that is through a local instance or remote instance.<\/p>\n<h2>Understanding the Dataset and Data Model<\/h2>\n<p>A great way to get your feet wet when it comes to Apache Spark is to get a sample dataset through the data science website,<br \/>\n<a href=\"https:\/\/www.kaggle.com\/\">Kaggle<\/a>. For this example we&#8217;re going to take a look at the sample dataset called<br \/>\n<a href=\"https:\/\/www.kaggle.com\/kaggle\/sf-salaries\">SF Salaries<\/a> which has information regarding how much money government<br \/>\nemployees in San Francisco are earning.<\/p>\n<p>From a data perspective there is a single comma separated value (CSV) file called <strong>salaries.csv<\/strong> with the following<br \/>\ncolumns in it:<\/p>\n<ol>\n<li>Id<\/li>\n<li>EmployeeName<\/li>\n<li>JobTitle<\/li>\n<li>BasePay<\/li>\n<li>OvertimePay<\/li>\n<li>OtherPay<\/li>\n<li>Benefits<\/li>\n<li>TotalPay<\/li>\n<li>TotalPayBenefits<\/li>\n<li>Year<\/li>\n<li>Notes<\/li>\n<li>Agency<\/li>\n<li>Status<\/li>\n<\/ol>\n<p>Working with the data in CSV format is near impossible. More so when it is massive amounts of it. Instead, this data is going to be<br \/>\nstored as NoSQL data so it can be later processed. We won&#8217;t get into the number crunching and querying here, but it will come in a<br \/>\nfuture article. Right now we just want to get it into NoSQL format.<\/p>\n<p>When loaded into Couchbase, each row of the CSV will look something like the following:<\/p>\n<pre><code>\r\n{\r\n    \"Id\": \"10029\",\r\n    \"EmployeeName\": \"FERGAL CLANCY\",\r\n    \"JobTitle\": \"BUILDING INSPECTOR\",\r\n    \"BasePay\": \"94529.22\",\r\n    \"OvertimePay\": \"0\",\r\n    \"OtherPay\": \"2502.6\",\r\n    \"Benefits\": \"\",\r\n    \"TotalPay\": \"97031.82\",\r\n    \"TotalPayBenefits\": \"97031.82\",\r\n    \"Year\": \"2011\",\r\n    \"Notes\": \"\",\r\n    \"Agency\": \"San Francisco\",\r\n    \"Status\": \"\"\r\n}\r\n<\/code><\/pre>\n<p>Yes, the above chunk of data is a JSON document, which is what Couchbase supports. Now that we know the data goals, we can begin<br \/>\nloading the CSV data into Couchbase with Apache Spark.<\/p>\n<h2>Transforming the Raw Data and Writing to Couchbase<\/h2>\n<p>To use Apache Spark in a Java application, a few dependencies must be included. We need to include Spark Core, Spark SQL, Spark CSV, and the<br \/>\nCouchbase Spark Connector. Since we&#8217;re using Maven, all can be included via the Maven <strong>pom.xml<\/strong> file. To include<br \/>\nSpark Core, include the following dependency in your Maven file:<\/p>\n<pre><code>\r\n\r\n    org.apache.spark\r\n    spark-core_2.10\r\n    1.6.1\r\n\r\n<\/code><\/pre>\n<p>Since the raw data will be in the form of CSV, we can use the convenience package for Spark called Spark CSV. The Maven dependency<br \/>\nfor Spark CSV can be added like this:<\/p>\n<pre><code>\r\n\r\n    com.databricks\r\n    spark-csv_2.10\r\n    1.4.0\r\n\r\n<\/code><\/pre>\n<p>The CSV data will be loaded into an Apache Spark DataFrame. If you&#8217;re unfamiliar with DataFrames, they can be queried using Spark<br \/>\nSQL. This is part of how we&#8217;ll get the data into Couchbase. To include Spark SQL into your project, add the Maven dependency<br \/>\nlike so:<\/p>\n<pre><code>\r\n\r\n    org.apache.spark\r\n    spark-sql_2.10\r\n    1.6.1\r\n    provided\r\n\r\n<\/code><\/pre>\n<p>Finally, Apache Spark needs to be connected to Couchbase Server. This can be done through the Couchbase Connector for Spark. To<br \/>\nadd this dependency into your Maven project, add the following to your <strong>pom.xml<\/strong> file:<\/p>\n<pre><code>\r\n\r\n    com.couchbase.client\r\n    spark-connector_2.10\r\n    1.1.0\r\n\r\n<\/code><\/pre>\n<p>All the project dependencies are good to go!<\/p>\n<p>To start loading CSV data via Java code, Apache Spark must first be configured within our project. This includes defining what Spark<br \/>\ninstance to use and what Couchbase bucket to store data into.<\/p>\n<pre><code>\r\nSparkConf conf = new SparkConf()\r\n        .setAppName(\"SF Salaries\")\r\n        .setMaster(\"local[*]\")\r\n        .set(\"com.couchbase.bucket.default\", \"\");\r\nJavaSparkContext javaSparkContext = new JavaSparkContext(conf);\r\n<\/code><\/pre>\n<p>The application name will be <strong>SF Salaries<\/strong> and the master Spark cluster will be the local machine<br \/>\nsince Spark will be running locally in this example. The Couchbase bucket to be used is once again the default bucket.<\/p>\n<p>To create a Spark DataFrame, a <code>SQLContext<\/code> must be created from the <code>JavaSparkContext<\/code>.<\/p>\n<pre><code>\r\nSQLContext sqlContext = new SQLContext(javaSparkContext);\r\n<\/code><\/pre>\n<p>Using the <strong>SQLContext<\/strong> the CSV data can be read like so:<\/p>\n<pre><code>\r\nDataFrame dataFrame = sqlContext.read()\r\n    .format(\"com.databricks.spark.csv\")\r\n    .option(\"inferSchema\", \"true\")\r\n    .option(\"header\", \"true\")\r\n    .load(\"PATH_TO_CSV_FILE\");\r\n<\/code><\/pre>\n<p>The read process will use the Spark CSV package and preserve the header information that exists at the top of the CSV file.<br \/>\nWhen read into a DataFrame, the CSV data is now something Couchbase can understand.<\/p>\n<p>An adjustment must be made to the id data. Spark will recognize it as an integer or numeric because this dataset only has<br \/>\nnumeric values as the column. Couchbase expects a string id.<\/p>\n<pre><code>\r\ndataFrame = dataFrame.withColumn(\"Id\", df.col(\"Id\").cast(\"string\"));\r\n<\/code><\/pre>\n<p>The DataFrame can now be prepared for saving to Couchbase.<\/p>\n<pre><code>\r\nDataFrameWriterFunctions dataFrameWriterFunctions = new DataFrameWriterFunctions(dataFrame.write());\r\nMap<\/code><\/pre>\n<p>With the DataFrame data piped into the appropriate <code>DataFrameWriterFunctions<\/code> object, the id value can be mapped to a<br \/>\ndocument id. The data at this point can be saved.<\/p>\n<pre><code>\r\ndataFrameWriterFunctions.couchbase(options);\r\n<\/code><\/pre>\n<p>Massive amounts of Couchbase documents will be saved to the bucket.<\/p>\n<h2>Running the Project with Apache Spark<\/h2>\n<p>Package the project into an executable JAR using Maven. The project can be executed after being packaged by doing something<br \/>\nlike this:<\/p>\n<pre><code>\r\n\/path\/to\/apache\/spark\/bin\/spark-submit --class \"com.app.Main\" target\/project-jar-with-dependencies.jar\r\n<\/code><\/pre>\n<p>Depending on the size of the dataset and the speed of your computer or server, the load process could take a while.<\/p>\n<h2>Conclusion<\/h2>\n<p>You just got a taste of loading dirty CSV data into Couchbase by using Apache Spark and the Couchbase Spark Connector. Spark was<br \/>\ndesigned to be able to quickly process massive amounts of data in real time. Combine it with Couchbase and its memory-centric<br \/>\narchitecture and you have a great package of software.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>I&#8217;ve been spending a lot of time working with Big Data tools lately, in particular Apache Spark. In case you&#8217;re unfamiliar, Apache Spark is an incredibly efficient tool for processing massive amounts of data. It performs significantly better than MapReduce, [&hellip;]<\/p>\n","protected":false},"author":63,"featured_media":13873,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1816,1818],"tags":[1613,1236,1614,1610],"ppma_author":[9032],"class_list":["post-2208","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-couchbase-server","category-java","tag-apache","tag-big-data","tag-csv","tag-spark"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.8 (Yoast SEO v25.8) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Load CSV Data into Couchbase using Apache Spark<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.couchbase.com\/blog\/load-csv-data-into-couchbase-using-apache-spark\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Load CSV Data into Couchbase using Apache Spark\" \/>\n<meta property=\"og:description\" content=\"I&#8217;ve been spending a lot of time working with Big Data tools lately, in particular Apache Spark. 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It performs significantly better than MapReduce, [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.couchbase.com\/blog\/load-csv-data-into-couchbase-using-apache-spark\/\" \/>\n<meta property=\"og:site_name\" content=\"The Couchbase Blog\" \/>\n<meta property=\"article:author\" content=\"https:\/\/www.facebook.com\/thepolyglotdeveloper\" \/>\n<meta property=\"article:published_time\" content=\"2016-03-29T15:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-06-23T12:20:40+00:00\" \/>\n<meta name=\"author\" content=\"Nic Raboy, Developer Advocate, Couchbase\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@nraboy\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Nic Raboy, Developer Advocate, Couchbase\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" 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