Category Archives: Big Data

for big data technical articles

A Handy Method of Accessing Data in Remote http Server in Java

In Java projects, sometimes accessing data in remote http server is required. The data can be of xml format or json format. The following will compare two accessing methods through an example. Here is a servlet which provides employee information query in json … Continue reading

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Using SQL in esProc (II)

5 Comparison between common SQL statements and esProc syntax 1) Select * from Query results are as follows:   2) Select … from         Get designated fields from the table. Both A2 and A3 have the same query … Continue reading

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Using SQL in esProc (I)

In esProc, we can use not only the SQL to retrieve data from databases, but also the preliminary database query results to perform further analyses and operations to solve some complicated problems which are difficult to deal with only with … Continue reading

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Program with Agile Syntax of esProc on Hadoop

Hadoop is an outstanding distributed computational system whose default developing mode is MapReduce coding. However, MapReduce is not specially designed for data computing. Plus, its syntax mechanism is cumbersome, the coding efficiency for computation is relatively low, and it is … Continue reading

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Writing Reusable codes with esProc on Hadoop

The MapReduce of Hadoop is a widely-used parallel computational framework. However, its code reuse mechanism is inconvenient, and it is quite cumbersome to pass parameters. Far different from our usual experience of calling the library function easily, I found both … Continue reading

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Realize In-Memory Computation with esProc on Hadoop

The low efficiency of Hadoop computation is an undeniable truth. We believe, one of the major reasons is that the underlying computational structure of MapReduce for Hadoop is basically of the external memory computation. The external memory computation implements the … Continue reading

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Implement Column Storage with esProc on Hadoop

The column storage is good, especially when there are lots of tabular fields (this is quite common). In querying, the data to traverse is far less than that on the row storage. Less data to traverse brings less I/O workloads … Continue reading

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