Generic Graph And Psets
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Covers various technical aspects which you need to take care while developing generic graphs and psets in Abinitio.
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Declaring a PointerTo define a pointer, use an asterisk, (), in t.pdf
Declaring a Pointer
To define a pointer, use an asterisk, (*), in the declaration to specify the variable will be a pointer
to the specified data type.
Recall that the name of an array holds the memory address of the first element of the array. The
second statement then would store the memory address of the first value in the variable valuePtr.
The name of a pointer is an identifier and must follow the rules for defining an identifier. Some
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pointer by its name. The naming of a pointer variable is a matter of programming style.
Once a pointer has been declared and initialized, it can be used to access the data to which it
points. In order to access this value, the dereference operator, *, must be used to prefix the name
of the pointer. From the code above, valuePtr holds the address of the first element of the values
array; therefore, *valuePtr will access the value 325. Add the following to main and execute the
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The output generated should look something like the following.
Where 0xbfad43e8 is a memory address displayed in hexadecimal.
Obtaining the Memory Address of a Variable
The address operator, &, is used to determine the memory address of a variable that is not an
array. Add code necessary to use fixed and setprecision, then add the following to main and run
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Using a Pointer to Alter Data
Just as the dereference operator is used to retrieve data, so too is it used to store data. Add the
following to function main and run the program.
The pointer, ratePtr, is used to change the data stored in payRate. The output of payRate
confirms the change.
Using a Pointer in an Expression
As previously shown, the value pointed to by a pointer variable can be retrieved by dereferencing
the pointer. In the above code, the retrieved value was simply displayed to the screen; however,
it can also be used in an expression. Add the following to main and execute the program.
Pointer Arithmetic
It is possible to add to or subtract from a memory address stored in a pointer. These actions can
be accomplished using the addition operator,+, subtraction operator, - and the increment and
decrement operators, ++ and --. This is helpful when accessing arrays via pointers.
The increment operator has been used to add the value 1 to a variable. When used with a pointer,
the increment operator adds the size of the data referenced to the memory address stored in the
pointer, effectively moving the pointer to the next data value. Building on the code from above,
add the following to main and execute the program.
We initialized valuePtr to the memory address of the first element of the array values.
Incrementing valuePtr instructs the computer to add four bytes, the size of an integer, to the
memory addressed stored in .
Twp Upgrading 10g To 11g What To Expect From Optimizer
The document discusses new initialization parameters and features in Oracle 11g for managing optimizer statistics. Key points include:
- Invisible indexes allow creating indexes without impacting execution plans until enabled.
- Pending statistics allow testing statistics before publishing to dictionaries.
- SQL plan management automatically captures plans as baselines and only uses known, verified plans.
- New preferences allow finer control over statistics collection parameters at the table, schema, and global levels.
2004 map reduce simplied data processing on large clusters (mapreduce)
The document describes MapReduce, a programming model and associated implementation for processing large datasets across distributed systems. It allows users to specify map and reduce functions to process key-value pairs. The runtime system handles parallelization across machines, partitioning data, scheduling execution, and handling failures. Hundreds of programs have been implemented using MapReduce at Google to process terabytes of data on thousands of machines.
Map reduce
The document describes MapReduce, a programming model and associated implementation for processing large datasets across distributed systems. MapReduce allows users to specify map and reduce functions to process key-value pairs. The runtime system automatically parallelizes and distributes the computation across clusters, handling failures and communication. Hundreds of programs have been implemented using MapReduce at Google to process terabytes of data on thousands of machines.
Php
PHP is a server-side scripting language used for web development. It allows developers to embed PHP code into HTML pages which is executed on the server to produce dynamic web pages. Some key points about PHP include:
- It is free, open source, and runs on many platforms including Windows and Linux.
- PHP code is easy to embed into HTML and syntax uses opening and closing tags.
- It can be used to connect to databases like MySQL and Oracle to dynamically display data on web pages.
- Common PHP functions include echo to output content, if statements for conditional logic, and arrays to store multiple values.
- Cookies can be used to store and retrieve data on the client-side browser to
Readme
This document summarizes the key information about installing and using SPSS 11.5 for Windows. It outlines the operating system requirements, installation process, new features in 11.5 including new data definition tools and two-step cluster analysis, and known issues such as limitations of using files saved in SAS formats in other applications and performance issues with large datasets. The document provides guidance on installation, configuration, use, and troubleshooting of SPSS 11.5 for Windows.
Lecture 1 mapreduce
The document describes MapReduce, a programming model and associated implementation for processing large datasets across distributed systems. It allows users to specify map and reduce functions to process key-value pairs. The runtime system handles parallelization across machines, partitioning data, scheduling execution, and handling failures. Hundreds of programs have been implemented using MapReduce at Google to process terabytes of data on thousands of machines.
SAP BPC Learning Notes and Insights.docx
This document provides an overview and lessons on key concepts in SAP BPC, including:
- BPC involves operations on the SAP GUI, web interface, and Excel. It is based on SAP BW and uses BW as its data source.
- Environments, dimensions, models, permissions, and the EPM plug-in are important BPC concepts. Environments contain models, dimensions define data views, and models correspond to BW info cubes.
- The EPM plug-in is used to build reports in Excel that access BPC data. Transformation and conversion files are used to load data into BPC from files via packages that invoke BW process chains.
Os Lonergan
The document discusses next generation data warehousing and business intelligence (BI) analytics. It outlines some of the challenges with scaling traditional BI systems to handle large and growing volumes of data. It then proposes using a massively parallel processing (MPP) database like Greenplum to enable scalable dataflow and embed analytics processing directly into the data warehouse. This would help address issues of data volume, processing time, and refreshing aggregated data for analytics servers. It presents an application profile for typical BI systems and discusses Greenplum's scaling technology using parallel queries and data streams. Finally, it introduces the draft gNet API for implementing parallel dataflows and analytics procedures directly in the MPP database.
Map reduce
This document introduces MapReduce, a programming model for processing large datasets across distributed systems. It describes how users write map and reduce functions to specify computations. The MapReduce system automatically parallelizes jobs by splitting input data, running the map function on different parts in parallel, collecting output, and running the reduce function to combine results. It handles failures and distribution of work across machines. Many common large-scale data processing tasks can be expressed as MapReduce jobs. The system has been used to process petabytes of data on thousands of machines at Google.
Map reduceoriginalpaper mandatoryreading
This document introduces MapReduce, a programming model and associated implementation for processing large datasets across distributed systems. The key aspects are:
1. Users specify map and reduce functions that process key-value pairs. The map function produces intermediate key-value pairs and the reduce function merges values for the same key.
2. The system automatically parallelizes the computation by partitioning input data and scheduling tasks on a cluster. It handles failures, data distribution, and load balancing.
3. The implementation runs on large Google clusters and is highly scalable, processing terabytes of data on thousands of machines. Hundreds of programs use MapReduce daily at Google.
Abstract.DOCX
The document discusses designing an application to import biological data files into a database table to allow for analysis of large datasets without memory issues, including developing modules to preprocess data files, import data into tables while handling different column orders and splitting data across multiple tables based on column limits, and providing features like undo/redo and standard analysis functions. The application "Database migration and management tool" (DBSERVER) was developed to address these issues and allow researchers to work more comfortably with large biological datasets.
Ac_2007_397_Use_of_Spreadsheets_with_Sca.pdf
This document discusses the use of spreadsheets and Visual Basic for Applications (VBA) programming to create scaled drawings within Excel spreadsheets to teach structural engineering concepts. It provides an overview of how to organize spreadsheet data, perform calculations in Excel, and then use VBA code to read the input data and create scaled structural drawings. Examples are given of spreadsheets that analyze and draw the design of a footing foundation and a reinforced concrete beam, allowing students to visualize engineering problems and solutions. The approach helps improve student learning by integrating calculations with graphical representations.
Question IYou are going to use the semaphores for process sy.docx
Question I
You are going to use the semaphores for process synchronization. Therefore, you are asked to develop a consumer and producer multithreaded program.
Let us assume, that we have a thread (producer, we will call it producer_thread) reading data (positive integer numbers) from the keyboard, entered by a user to be stored in an array (dynamic array). (Assume that the array can hold all numbers entered without overflow.)
Another thread (consumer, we will call it consumer_thread) should read data from the array and write them into a file. This thread should run concurrently with the producer (producer_thread).
Your program should make sure that the consumer_thread can read from the array only after the producer_thread has stored new data. Both threads will stop when the user enters a negative number (well synchronized).
Another thread (testing_thread) should start reading the array data as well as the file data and display them on the screen in order to verify if the consumer and producer have worked in a correctly synchronized fashion. This thread should not be synchronized with other threads, it is intended for testing that consumer thread is synchronized with produce thread.
Provide your tutor with the source code as well as screen snapshots that show the work of the testing_thread.
1
TM298_TMA_Q2/build.xml
Builds, tests, and runs the project TM298_TMA_Q2.
TM298_TMA_Q2/manifest.mf
Manifest-Version: 1.0
X-COMMENT: Main-Class will be added automatically by build
TM298_TMA_Q2/nbproject/build-impl.xml
...
phoenix-on-calcite-hadoop-summit-2016
This document summarizes a presentation on using Apache Calcite for cost-based query optimization in Apache Phoenix. Key points include:
- Phoenix is adding Calcite's query planning capabilities to improve performance and SQL compliance over its existing query optimizer.
- Calcite models queries as relational algebra expressions and uses rules, statistics, and a cost model to choose the most efficient execution plan.
- Examples show how Calcite rules like filter pushdown and exploiting sortedness can generate better plans than Phoenix's existing optimizer.
- Materialized views and interoperability with other Calcite data sources like Apache Drill are areas for future improvement beyond the initial Phoenix integration.
Cost-based Query Optimization
This document summarizes a presentation on using Apache Calcite for cost-based query optimization in Apache Phoenix. Key points include:
- Phoenix is adding Calcite's query planning capabilities to improve performance and SQL compliance over its existing query optimizer.
- Calcite models queries as relational algebra expressions and uses rules, statistics, and a cost model to choose the most efficient execution plan.
- Examples show how Calcite rules like filter pushdown and exploiting sortedness can generate better plans than Phoenix's existing optimizer.
- Materialized views and interoperability with other Calcite data sources like Apache Drill are areas for future improvement beyond the initial Phoenix integration.
Cost-based Query Optimization in Apache Phoenix using Apache Calcite
This document summarizes a presentation on using Apache Calcite for cost-based query optimization in Apache Phoenix. Key points include:
- Phoenix is adding Calcite's query planning capabilities to improve performance and SQL compliance over its existing query optimizer.
- Calcite models queries as relational algebra expressions and uses rules, statistics, and a cost model to choose the most efficient execution plan.
- Examples show how Calcite rules like filter pushdown and exploiting sortedness can generate better plans than Phoenix's existing optimizer.
- Materialized views and interoperability with other Calcite data sources like Apache Drill are areas for future improvement beyond the initial Phoenix+Calcite integration.
Cost-Based query optimization
The document discusses how Apache Phoenix, an SQL query engine for Apache HBase, is integrating with Apache Calcite, an open source query optimization framework, to improve query optimization capabilities. Key points include:
- Phoenix will leverage Calcite's SQL parser, validator, relational algebra, and cost-based query optimization capabilities to improve performance and SQL standard compliance.
- Calcite models query optimizations as pluggable rules and uses a cost model and statistics to evaluate optimization alternatives. This will allow Phoenix to make more informed decisions around optimizations like index usage, join algorithms, and predicate pushdown.
- Features like materialized views in Calcite will enable new indexing capabilities in Phoenix like defining indexes on query results to better
Similar to Generic Graph And Psets (20)
Declaring a PointerTo define a pointer, use an asterisk, (), in t.pdf
Declaring a PointerTo define a pointer, use an asterisk, (), in t.pdf
Twp Upgrading 10g To 11g What To Expect From Optimizer
Twp Upgrading 10g To 11g What To Expect From Optimizer
2004 map reduce simplied data processing on large clusters (mapreduce)
2004 map reduce simplied data processing on large clusters (mapreduce)
Question IYou are going to use the semaphores for process sy.docx
Question IYou are going to use the semaphores for process sy.docx
Cost-based Query Optimization in Apache Phoenix using Apache Calcite
Cost-based Query Optimization in Apache Phoenix using Apache Calcite
Generic Graph And Psets
- 10. EME view of distinct instances of generic graph: As above different data lineage are achieved in two instances of the same graph in EME.