This white paper focuses on handling data corruption in Elasticsearch. It describes how to recover data from corrupted indices of Elasticsearch and re-index that data in a new index. The paper also guides you about Lucene’s index terminology
Apache Lucene starter for developers and novices, illustrates simple code example. complete source code can be found on - https://github.com/ani03sha/lucene-starter
In this playlist
https://youtube.com/playlist?list=PLT...
I'll illustrate algorithms and data structures course, and implement the data structures using java programming language.
the playlist language is arabic.
The Topics:
--------------------
1- Arrays
2- Linear and Binary search
3- Linked List
4- Recursion
5- Algorithm analysis
6- Stack
7- Queue
8- Binary search tree
9- Selection sort
10- Insertion sort
11- Bubble sort
12- merge sort
13- Quick sort
14- Graphs
15- Hash table
16- Binary Heaps
Reference : Object-Oriented Data Structures Using Java - Third Edition by NELL DALE, DANEIEL T.JOYCE and CHIP WEIMS
Slides is owned by College of Computing & Information Technology
King Abdulaziz University, So thanks alot for these great materials
Prepares the students for (and is a prerequisite for) the more advanced material students will encounter in later courses. Data structures organize data Þ more efficient programs.
Big Data - Load CSV File & Query the EZ way - HPCC SystemsFujio Turner
A "How To" to load CSV files into HPCC Systems and query them. You can use this method to migrate your RDBMS data ,MySQL / Oracle / SQL, into HPCC Systems.
Apache Lucene starter for developers and novices, illustrates simple code example. complete source code can be found on - https://github.com/ani03sha/lucene-starter
In this playlist
https://youtube.com/playlist?list=PLT...
I'll illustrate algorithms and data structures course, and implement the data structures using java programming language.
the playlist language is arabic.
The Topics:
--------------------
1- Arrays
2- Linear and Binary search
3- Linked List
4- Recursion
5- Algorithm analysis
6- Stack
7- Queue
8- Binary search tree
9- Selection sort
10- Insertion sort
11- Bubble sort
12- merge sort
13- Quick sort
14- Graphs
15- Hash table
16- Binary Heaps
Reference : Object-Oriented Data Structures Using Java - Third Edition by NELL DALE, DANEIEL T.JOYCE and CHIP WEIMS
Slides is owned by College of Computing & Information Technology
King Abdulaziz University, So thanks alot for these great materials
Prepares the students for (and is a prerequisite for) the more advanced material students will encounter in later courses. Data structures organize data Þ more efficient programs.
Big Data - Load CSV File & Query the EZ way - HPCC SystemsFujio Turner
A "How To" to load CSV files into HPCC Systems and query them. You can use this method to migrate your RDBMS data ,MySQL / Oracle / SQL, into HPCC Systems.
Elasticsearch, a distributed search engine with real-time analyticsTiziano Fagni
An overview of Elasticsearch: main features, architecture, limitations. It includes also a description on how to query data both using REST API and using elastic4s library, with also a specific interest into integration of the search engine with Apache Spark.
This presentation slide is a condensed theoretical overview of Elasticsearch prepared by going through the official ES Definitive Guide and Practical Guide.
Recursively Searching Files and DirectoriesSummaryBuild a class .pdfmallik3000
Recursively Searching Files and Directories
Summary
Build a class and a driver for use in searching your computer’s secondary storage (hard disk or
flash memory) for a specific file from a set of files indicated by a starting path. Lets start by
looking at a directory listing. Note that every element is either a file or a directory.
Introduction and Driver
In this assignment, your job is to write a class that searches through a file hierarchy (a tree) for a
specified file. Your FindFile class will search a directory (and all subdirectories) for a target file
name.
For example, in the file hierarchy pictured above, the file “lesson.css” will be found once in a
directory near the root or top-level drive name (e.g. “C:\\”) . Your FindFile class will start at the
path indicated and will search each directory and subdirectory looking for a file match. Consider
the following code that could help you build your Driver.java:
String targetFile = “lesson.css”;
String pathToSearch =”
C:\\\\WCWC”; FindFile finder = new FindFile(MAX_NUMBER_OF_FILES_TO_FIND);
Finder.directorySearch(targetFile, pathToSearch);
File Searching
In general, searching can take multiple forms depending on the structure and order of the set to
search. If we can make promises about the data (this data is sorted, or deltas vary by no more
than 10, etc.), then we can leverage those constraints to perform a more efficient search. Files in
a file system are exposed to clients of the operating system and can be organized by filename,
file creation date, size, and a number of other properties. We’ll just be interested in the file
names here, and we’ll want perform a brute force (i.e., sequential) search of these files looking
for a specific file. The way in which we’ll get file information from the operating system will
involve no ordering; as a result, a linear search is the best we can do. We’d like to search for a
target file given a specified path and return the location of the file, if found. You should sketch
out this logic linearly before attempting to tackle it recursively.
FindFile Class Interface
FindFile(int maxFiles): This constructor accepts the maximum number of files to find.
void directorySearch(String target, String dirName): The parameters are the target file name to
look for and the directory to start in.
int getCount(): This accessor returns the number of matching files found
String[] getFiles(): This getter returns the array of file locations, up to maxFiles in size.
Requirements
Your program should be recursive.
You should build and submit at least two files: FindFile.java and Driver.java.
Throw an exception (IllegalArgumentException) if the path passed in as the starting directory is
not a valid directory.
Throw an exception if you\'ve found the MAX_NUMBER_OF_FILES_TO_FIND and catch and
handle this in your main driver. Your program shouldn\'t crash but rather exit gracefully in the
unusual situation that we\'ve discovered the maximum number of files we were interest.
A FAST METHOD FOR IMPLEMENTATION OF THE PROPERTY LISTS IN PROGRAMMING LANGUAGESijpla
One of the major challenges in programming languages is to support different data structures and their
variations in both static and dynamic aspects. One of the these data structures is the property list which
applications use it as a convenient way to store, organize, and access standard types of data. In this paper,
the standards methods for implementation of the Property Lists, including the Static Array, Link List, Hash
and Tree are reviewed. Then an efficient method to implement the property list is presented. The
experimental results shows that our method is fast compared with the existing methods.
A brief presentation outlining the basics of elasticsearch for beginners. Can be used to deliver a seminar on elasticsearch.(P.S. I used it) Would Recommend the presenter to fiddle with elasticsearch beforehand.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
Elasticsearch as a search alternative to a relational databaseKristijan Duvnjak
The volume of data that we are working with is growing every day, the size of data is pushing us to find new intelligent solutions for problem’s put in front of us. Elasticsearch server has proved it self as an excellent full text search solution for big volume’s of data.
Future-Proof Your Streaming Analytics Architecture- StreamAnalytix WebinarImpetus Technologies
Future-Proof Your Streaming Analytics Architecture- StreamAnalytix Webinar
View the webcast on http://bit.ly/1HFD8YR
The speakers from Forrester and Impetus talk about the options and optimal architecture to incorporate real-time insights into your apps that provisions benefitting from future innovation also.
Deep Learning: Evolution of ML from Statistical to Brain-like Computing- Data...Impetus Technologies
Presentation on 'Deep Learning: Evolution of ML from Statistical to Brain-like Computing'
Speaker- Dr. Vijay Srinivas Agneeswaran,Director, Big Data Labs, Impetus
The main objective of the presentation is to give an overview of our cutting edge work on realizing distributed deep learning networks over GraphLab. The objectives can be summarized as below:
- First-hand experience and insights into implementation of distributed deep learning networks.
- Thorough view of GraphLab (including descriptions of code) and the extensions required to implement these networks.
- Details of how the extensions were realized/implemented in GraphLab source – they have been submitted to the community for evaluation.
- Arrhythmia detection use case as an application of the large scale distributed deep learning network.
SPARK USE CASE- Distributed Reinforcement Learning for Electricity Market Bi...Impetus Technologies
SPARK SUMMIT SESSION -
A majority of the electricity in the U.S. is traded in independent system operator (ISO) based wholesale markets. ISO-based markets typically function in a two-step settlement process with day-ahead (DA) financial settlements followed by physical real-time (spot) market settlements for electricity. In this work, we focus on obtaining equilibrium bidding strategies for electricity generators in DA markets. Electricity prices in DA markets are determined by the ISO, which matches competing supply offers from power generators with demand bids from load serving entities. Since there are multiple generators competing with one another to supply power, this can be modeled as a competitive Markov decision problem, which we solve using a reinforcement learning approach. For power networks of realistic sizes, the state-action space could explode, making the RL procedure computationally intensive. This has motivated us to solve the above problem over Spark. The talk provides the following takeaways:
1. Modeling the day-ahead market as a Markov decision process
2. Code sketches to show the markov decision process solution over Spark and Mahout over Apache Tez
3. Performance results comparing Mahout over Apache Tez and Spark.
Real-time Streaming Analytics: Business Value, Use Cases and Architectural Co...Impetus Technologies
Impetus webcast ‘Real-time Streaming Analytics: Business Value, Use Cases and Architectural Considerations’ available at http://bit.ly/1i6OrwR
The webinar talks about-
• How business value is preserved and enhanced using Real-time Streaming Analytics with numerous use-cases in different industry verticals
• Technical considerations for IT leaders and implementation teams looking to integrate Real-time Streaming Analytics into enterprise architecture roadmap
• Recommendations for making Real-time Streaming Analytics – real – in your enterprise
• Impetus StreamAnalytix – an enterprise ready platform for Real-time Streaming Analytics
Leveraging NoSQL Database Technology to Implement Real-time Data Architecture...Impetus Technologies
Impetus webcast "Leveraging NoSQL Database Technology to Implement Real-time Data Architectures” available at http://bit.ly/1g6Eaj4
This webcast:
• Presents trade-offs of using different approaches to achieve a real-time architecture
• Closely examines an implementation of a NoSQL based real-time architecture
• Shares specific capabilities offered by NoSQL Databases that enable cost and reliability advantages over other techniques
Maturity of Mobile Test Automation: Approaches and Future Trends- Impetus Web...Impetus Technologies
Impetus webcast " Maturity of Mobile Test Automation: Approaches and Future Trends " available at http://lf1.me/Pxb/
This Impetus webcast talks about:
• Mobile test automation challenges
• Evolution of test automation challenges from Unit tests to image based and object comparison methods
• What next?
• Impetus solution approach for comprehensive mobile testing automation
The Shared Elephant - Hadoop as a Shared Service for Multiple Departments – I...Impetus Technologies
For Impetus’ White Papers archive, visit- http://lf1.me/drb/
This white paper talks about the design considerations for enterprises to run Hadoop as a shared service for multiple departments.
As Hadoop becomes more mainstream and indispensable to enterprises, it is imperative that they build, operate and scale shared Hadoop clusters. The design considerations discussed in this paper will help enterprises accomplish the essential mission of running multi-tenant, multi-use Hadoop clusters at scale.
The white paper talks about Identity, Security, Resource Sharing, Monitoring and Operations on the Central Service.
For Impetus’ White Papers archive, visit- http://lf1.me/drb/
Performance Testing of Big Data Applications - Impetus WebcastImpetus Technologies
Impetus webcast "Performance Testing of Big Data Applications" available at http://lf1.me/cqb/
This Impetus webcast talks about:
• A solution approach to measure performance and throughput of Big Data applications
• Insights into areas to focus for increasing the effectiveness of Big Data performance testing
• Tools available to address Big Data specific performance related challenges
Real-time Predictive Analytics in Manufacturing - Impetus WebinarImpetus Technologies
Impetus webcast "Real-time Predictive Analytics in Manufacturing" available at http://lf1.me/hqb/
This Impetus webcast talks about:
• The business value of predictive analytics
• How real-time analytics is enabling ‘intelligent-data’ driven manufacturing
• A Reference Architecture and real world examples based on the experiences of Impetus Big Data architects
• A step-by-step guide for successfully implementing a predictive analytics solution
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Impetus White Paper- Handling Data Corruption in Elasticsearch
1. www.impetus.com
Handling
Data Corruption
in Elasticsearch
This white paper focuses on handling data corruption in Elasticsearch. It describes
how to recover data from corrupted indices of Elasticsearch and
re-index that data in a new index.
The paper also guides you about Lucene’s index terminology.
2. Elasticsearch is an Open Source, schema free, and restful search engine built
on Apache Lucene. It has a stand-alone database server for data intake and
storage in a format optimized for language-based searches and a JSON-based
access API for ease-of-use.
An Elasticsearch cluster can be horizontally scaled by adding a new node at
runtime to cater to the increased volume of data as per need. It uses
zen-discovery for internal co-ordination between the nodes in a cluster.
Failover and high availability can be achieved by using replication and using a
distributed cluster setup.
What is Elasticsearch?
2
Data Replication
Data replication is used for high data availability. For example, if the replication
factor is 1, then there will be one replica of each primary shard. In case of
replication, there are rare chances of data loss. If the primary shard fails, then a
replica of that shard is used to manage the cluster in a stable state. If we
perform any query or other operation, it will be served by that shard. This
enables us to recover the data in case of data replication.
However, data replication has its own set of limitations like storage. In such
cases, where users do not want to replicate due to storage issues, recovering
the data of index if any primary shard gets corrupt is a major challenge.
Data Recovery from Corrupted Index
Data can be recovered from corrupted index by reading data files of an index
and re-indexing it to a new index. However, to recover the data, the user needs
to store all the fields in Elasticsearch, which stores and indexes the data as
Lucene files.
Each shard in the index may have multiple segments, which, if corrupt, makes
the index unstable. To make the data searchable, index must be in stable
state, which can be ensured in two ways:
• Run optimize operation on an index and merge all segments to one in a
shard. This may cause data loss since it removes the reference of that
particular segment of which data got corrupt.
• Recover the data by reading data files and re-indexing the same.
3. 3
Lucene uses many files for an index. The table below highlights the four major
files that can be used to recover the data:
Name Extension Brief Description
Fields .fnm Stores information about the fields
Field Index .fdx Contains pointers to field data
Field Data .fdt The stored fields for documents
Segment Info .si Stores metadata about a segment
Note: If any of these files are corrupt, there are chances of data loss in case of
zero replication.
There are four steps to recover data from the corrupted index, which are
detailed below:
Before data recovery, it is important to identify the shard id of corrupted shard
of an index. Corrupted shards can be identified using UNASSIGNED state of
shard. However, you need to ensure that the whole cluster is in running state
and all the nodes are up. You can find a list of unassigned shards from
Elasticsearch cluster state. There are different ways of getting cluster state, for
example using curl request:
$ curl -XGET 'http://localhost:9200/_cluster/state'
Identify corrupted shards of index
You can identify the shard directory by logic dependent on the Elasticsearch
home and cluster name. If there is only one node on the machine, use the
shard id and index name to identify the shard directory.
Identify shard’s index directory
String shardDir=new
StringBuilder().append(esHome).append("/").append(dataDi
rectryName).append("/").append(clusterName).append("/nod
es/0/indices/").append(indexName).append("/").append(sha
rdId).append("/index").toString();
4. 4
public void readAndReindexData(String indexName, String
indexDir,String newIndexName) {
try {
Codec codec = new Lucene42Codec();
File indexDirectory = new File(indexDir);
Directory dir = FSDirectory.open(indexDirectory);
List<String> segmentList = new ArrayList<String>();
/* Identify segment list by listing files in shard
directory. Each segment will have .si file */
for (File f : FileUtils.listFiles(indexDirectory, new
RegexFileFilter("_.*.si"), null)) {
String s = f.getName();
segmentList.add(s.substring(0,
s.indexOf('.')));
}
int total=0;
// Iterate over each segment of that shard and reindex
that
for (String segmentName : segmentList) {
try{
IOContext ioContext = new IOContext();
SegmentInfo segmentInfos =
codec.segmentInfoFormat().getSegmentInfoReader().read(dir,
segmentName, ioContext);
Directory segmentDir;
if (segmentInfos.getUseCompoundFile()) {
segmentDir = new CompoundFileDirectory(dir,
IndexFileNames.segmentFileName(segmentName, "",
IndexFileNames.COMPOUND_FILE_EXTENSION), ioContext,
false);
} else {
segmentDir = dir;
}
// Collect fields information
FieldInfos fieldInfos =
codec.fieldInfosFormat().getFieldInfosReader().read(segmen
tDir, segmentName, ioContext);
StoredFieldsReader storedFieldsReader =
codec.storedFieldsFormat() .fieldsReader(segmentDir,
segmentInfos, fieldInfos, ioContext);
Read data of corrupted shard using .fdt, .fdx files
There may be number of segments in an index, which one needs to identify
and then read the data of a particular segment. After reading a document from
a segment, you can insert the document into another index.
A sample code to read data from index using .fdt, .fdx, .fnm, and .si files is
given below:
5. 5
total=total+segment?Infos.getDocCount();
for (int i = 0; i < segmentInfos.getDocCount(); ++i) {
try {
DocumentStoredFieldVisitor visitor = new
DocumentStoredFieldVisitor();
storedFieldsReader.visitDocument(i, visitor);
Document doc = visitor.getDocument();
// Get list of fields of a document
List<IndexableField> list = doc.getFields();
Map<String, Object> tempMap = new HashMap<String,
Object>();
for (IndexableField indexableField : list) {
tempMap.put(indexableField.name(),
indexableField.stringValue());
}
// Re-index the document in new index
this.index(tempMap,newIndexName);
} catch (Exception e) {
System.out.println("Couldn't get document " + i + ",
stored fields corruption.");
}}}catch(Exception e){}
}
System.out.println(total+" documents recovered.");
}catch (Exception e) {
e.printStackTrace();
}
}
When you read a document from the index, the document contains uid and
source fields. You can get the document id from uid field. Before indexing the
document, you need to remove the uid and source field, because Lucene add
these two fields by default when any document is indexed.
Re-index data in new index