bigdata

In case of "Name Node", what gets stored in main memory and what gets stored in secondary memory ( hard disk ). What we mean by "file to block mapping" ? What exactly is fsimage and edit logs ? In case of "Name Node", what gets stored in main memory and what gets stored in secondary memory ( hard disk ). The file to block mapping, locations of blocks on data nodes, active data nodes, a bunch of other metadata is all stored in memory on the NameNode. When you check the NameNode status website, pretty much all of that information is stored in memory somewhere. The only thing stored on disk is

I have an RDD whose elements are of type (Long, String). For some reason, I want to save the whole RDD into the HDFS, and later also read that RDD back in a Spark program. Is it possible to do that? And if so, how? It is possible. In RDD you have saveAsObjectFile and saveAsTextFile functions. Tuples are stored as (value1, value2) , so you can later parse it. Reading can be done with textFile function from SparkContext and then .map to eliminate () So: Version 1: rdd.saveAsTextFile ("hdfs:///test1/"); // later, in other program val newRdds = sparkContext.textFile("hdfs:///test1/part-*").map (x

I have some expirience with Apache Spark and Spark-SQL. Recently I've found Apache Drill project. Could you describe me what are the most significant advantages/differences between them? I've already read Fast Hadoop Analytics (Cloudera Impala vs Spark/Shark vs Apache Drill) but this topic is still unclear for me. Here's an article I came across that discusses some of the SQL technologies: http://www.zdnet.com/article/sql-and-hadoop-its-complicated/ Drill is fundamentally different in both the user's experience and the architecture. For example: Drill is a schema-free query engine. For

This question was migrated from Cross Validated because it can be answered on Stack Overflow. Migrated 7 years ago . Learn more . I have a very large training set (~2Gb) in a CSV file. The file is too large to read directly into memory ( read.csv() brings the computer to a halt) and I would like to reduce the size of the data file using PCA. The problem is that (as far as I can tell) I need to read the file into memory in order to run a PCA algorithm (e.g., princomp() ). I have tried the bigmemory package to read the file in as a big.matrix , but princomp doesn't function on big.matrix objects

I have a big table in hbase that name is UserAction, and it has three column families(song,album,singer). I need to fetch all of data from 'song' column family as a JavaRDD object. I try this code, but it's not efficient. Is there a better solution to do this ? static SparkConf sparkConf = new SparkConf().setAppName("test").setMaster( "local[4]"); static JavaSparkContext jsc = new JavaSparkContext(sparkConf); static void getRatings() { Configuration conf = HBaseConfiguration.create(); conf.set(TableInputFormat.INPUT_TABLE, "UserAction"); conf.set(TableInputFormat.SCAN_COLUMN_FAMILY, "song");

I have successfully created and added Dynamic partitions in an Internal table in hive. i.e. by using following steps: 1-created a source table 2-loaded data from local into source table 3- created another table with partitions - partition_table 4- inserted the data to this table from source table resulting in creation of all the partitions dynamically My question is, how to perform this in external table? I read so many articles on this, but i am confused , that do I have to specify path to the already existing partitions for creating partitions for external table?? example: Step 1: create