apache-drill

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

I want to do some "near real-time" data analysis (OLAP-like) on the data in a HDFS. My research showed that the three mentioned frameworks report significant performance gains compared to Apache Hive. Does anyone have some practical experience with either one of those? Not only concerning performance, but also with respect of stability? Comparison between Hive and Impala or Spark or Drill sometimes sounds inappropriate to me. The goals behind developing Hive and these tools were different. Hive was never developed for real-time, in memory processing and is based on MapReduce. It was built for

With apache drill 1.2, we can query over RDBMS data. Check more here: https://drill.apache.org/blog/2015/10/16/drill-1.2-released/ so, I tried to add a plugin for MySQL. I am doing it using the web client. I created a plugin with name mysql and added following configurations: { "type": "jdbc", "driver": "com.mysql.jdbc.Driver", "uri": "jdbc:mysql://<IP>:3306/classicmodels", "username": "root", "password": "root", "enabled": true } Also, I added mysql.jar in /apache-drill-1.2.0/jars/3rdparty It is showing error: (Invalid JSON mapping) Any pointer on this. Is there any documentation for that?

I would like to create a Spark SQL DataFrame from the results of a query performed over CSV data (on HDFS) with Apache Drill. I successfully configured Spark SQL to make it connect to Drill via JDBC: Map<String, String> connectionOptions = new HashMap<String, String>(); connectionOptions.put("url", args[0]); connectionOptions.put("dbtable", args[1]); connectionOptions.put("driver", "org.apache.drill.jdbc.Driver"); DataFrame logs = sqlc.read().format("jdbc").options(connectionOptions).load(); Spark SQL performs two queries: the first one to get the schema, and the second one to retrieve the

I want to connect to Drill by Java app, and so far I was trying to use JDBC to do it and I'm using example from https://github.com/vicenteg/DrillJDBCExample , but... when I change DB_URL static variable to "jdbc:drill:zk=local" and start app i get exception: java.sql.SQLNonTransientConnectionException: Running Drill in embedded mode using Drill's jdbc-all JDBC driver Jar file alone is not supported. and so far I didn't found any workaround. Any idea how to connect to Drill in embedded mode? I don't want to set up distributed mode so far. There is truly not much about it on the web. Any help

I tried using apache-drill to run a simple join-aggregate query and the speed wasn't really good. my test query was: SELECT p.Product_Category, SUM(f.sales) FROM facts f JOIN Product p on f.pkey = p.pkey GROUP BY p.Product_Category Where facts has about 422,000 rows and product has 600 rows. the grouping comes back with 4 rows. First I tested this query on SqlServer and got a result back in about 150ms. With drill I first tried to connect directly to SqlServer and run the query, but that was slow (about 5 sec). Then I tried saving the tables into json files and reading from them, but that was

I'm using drill in embedded mode, and I can't figure out how to save query output other than copy and pasting it. If you're using sqlline, you can create a new table as CSV as follows: use dfs.tmp; alter session set `store.format`='csv'; create table dfs.tmp.my_output as select * from cp.`employee.json`; Your CSV file(s) will appear in /tmp/my_output. You can specify !record <file_path> to save all output to particular file. Drill docs Andrew Scott Evans If you are using SQLLINE use !record . If you are using a set of queries, you need to specify the exact schema to use. This can be done using