The Lucene KV-Store is a high-performance key-value store that uses Lucene's file access APIs and concepts like segments and merges to provide efficient reads, writes, and durability guarantees over large volumes of data. It hashes keys to determine the segment file and offset for lookup or write. Reads require at most one disk seek while writes append to the active segment. Segments are periodically merged in the background to reclaim space.
Mark Harwood - Building Entity Centric Indexes - NoSQL matters Dublin 2015NoSQLmatters
Sometimes we need to step back and take a look at the bigger picture - not just counting huge piles of individual log records, but reasoning about the behaviors of the people who are ultimately generating this firehose of data. While your DevOps folks care deeply about log records from a machine utlization perspective, marketing wants to know what these records tell us about the customers' needs. Elasticsearch Aggregations are a great feature but are not a panacea. We can happily use them to summarise complex things like the number of web requests per day broken down by geography and browser type on a busy website, but we would quickly run out of memory if we tried to calculate something as simple as a single number for the average duration of visitor web sessions when using the very same dataset. Why does this occur? A web session duration is an example of a behavioural attribute not held on any one log record; it has to be derived by finding the first and last records for each session in our weblogs, requiring some complex query expressions and a lot of memory to connect all the data points. We can maintain a more useful joined-up-picture if we run an ongoing background process to fuse related events from one index into ?entity-centric? summaries in another index e.g: • Web log events summarised into ?web session? entities • Road-worthiness test results summarised into ?car? entities • Reviews in a marketplace summarised into a ?reviewer? entity Using real data, this session will demonstrate how to incrementally build entity-centric indexes alongside event-centric indexes by using simple scripts to uncover interesting behaviours that accumulate over time. We'll explore: • Which cars are driven long distances after failing roadworthiness tests? • Which website visitors look to be behaving like ?bots?? • Which seller in my marketplace has employed an army of ?shills? to boost his feedback rating? Attendees will leave this session with all the tools required to begin building entity-centric indexes and using that data to derive richer business insights across every department in their organization.
Mark Harwood - Building Entity Centric Indexes - NoSQL matters Dublin 2015NoSQLmatters
Sometimes we need to step back and take a look at the bigger picture - not just counting huge piles of individual log records, but reasoning about the behaviors of the people who are ultimately generating this firehose of data. While your DevOps folks care deeply about log records from a machine utlization perspective, marketing wants to know what these records tell us about the customers' needs. Elasticsearch Aggregations are a great feature but are not a panacea. We can happily use them to summarise complex things like the number of web requests per day broken down by geography and browser type on a busy website, but we would quickly run out of memory if we tried to calculate something as simple as a single number for the average duration of visitor web sessions when using the very same dataset. Why does this occur? A web session duration is an example of a behavioural attribute not held on any one log record; it has to be derived by finding the first and last records for each session in our weblogs, requiring some complex query expressions and a lot of memory to connect all the data points. We can maintain a more useful joined-up-picture if we run an ongoing background process to fuse related events from one index into ?entity-centric? summaries in another index e.g: • Web log events summarised into ?web session? entities • Road-worthiness test results summarised into ?car? entities • Reviews in a marketplace summarised into a ?reviewer? entity Using real data, this session will demonstrate how to incrementally build entity-centric indexes alongside event-centric indexes by using simple scripts to uncover interesting behaviours that accumulate over time. We'll explore: • Which cars are driven long distances after failing roadworthiness tests? • Which website visitors look to be behaving like ?bots?? • Which seller in my marketplace has employed an army of ?shills? to boost his feedback rating? Attendees will leave this session with all the tools required to begin building entity-centric indexes and using that data to derive richer business insights across every department in their organization.
DocValues aka. Column Stride Fields in Lucene 4.0 - By Willnauer Simonlucenerevolution
See conference video - http://www.lucidimagination.com/devzone/events/conferences/revolution/2011
Lucene 4.0 is on its way to deliver a tremendous amount of new features and improvements. Beside
Real-Time Search & Flexible Indexing DocValues aka. Column Stride Fields is one of the “next
generation” features. DocValues enable Lucene to efficiently store and retrieve type-safe Document
& Value pairs in a column stride fashion either entirely memory resident random access or disk
resident iterator based without the need to un-invert fields. Its final goal is to provide a
independently update-able per document storage for scoring, sorting or even filtering. This talk will
introduce the current state of development, implementation details, its features and how DocValues
have been integrated into Lucene’s Codec API for full extendability.
Lucene 4.0 is on its way to deliver a tremendous amount of new features and improvements. Beside Real-Time Search & Flexible Indexing DocValues aka. Column Stride Fields is one of the "next generation" features
Presented at a guest lecture at the Rijksuniversiteit Groningen as part of the web and cloud computing master course.
I presented a architecture for and working implementation of doing Hadoop based typeahead style search suggestions. There is a companion github repo with the code and config at: https://github.com/friso/rug (there's no documentation, though).
Quontra Solutions offers Job oriented Linux online training with updated technologies. For more info about our Linux online training contact us directly. We are providing Linux online training to all students throughout worldwide by real time faculties. Our Linux training strengthens your skills and knowledge which will helps you to gain a competitive advantage in starting your career. Outclasses will help you to gain knowledge on real time scenario. It will be most use full to boost up your career.
Our training sessions are designed in such a way that all the students can be convenient with the training schedules and course timings.
Along with Training, we also conduct several mock interviews along with Job Placement Assistance. Attend Free Demo before joining the class.
Our Features:
• Real world projects to get practical based experience
• Online tests to explore the resource learning
• Experienced certified trainers as instructors
• One to one personalized training with desktop access
• Case studies and state of art library to access study material
• Resume build assistance to win in interviews
Contact us:
Simson Andrew
Email: info@quontrasolutions.com
web: www.quontrasolutions.com
Quand Swift a été annoncé en 2014, personne n'aurait imaginé qu'un jour on aurait pu se servir de ce langage pour réaliser une application... côté serveur !
Ce talk a pour but de démontrer comment et dans quelle mesure nous pouvons écrire et deployer une application Web en utilisant Swift 3.0, en utilisant les plates-formes proposées par le marché. Nous découvrirons également les forces et faiblesses actuelles, les frameworks et les outils à utiliser aujourd'hui, ainsi que les extensions pour connecter notre application Swift côté serveur à des services tierces, tels que les systèmes de gestion de base de données.
Par Simone Civetta, Développeur iOS chez Xebia.
Introduction to DTrace (Dynamic Tracing), written by Brendan Gregg and delivered in 2007. While aimed at a Solaris-based audience, this introduction is still largely relevant today (2012). Since then, DTrace has appeared in other operating systems (Mac OS X, FreeBSD, and is being ported to Linux), and, many user-level providers have been developed to aid tracing of other languages.
DocValues aka. Column Stride Fields in Lucene 4.0 - By Willnauer Simonlucenerevolution
See conference video - http://www.lucidimagination.com/devzone/events/conferences/revolution/2011
Lucene 4.0 is on its way to deliver a tremendous amount of new features and improvements. Beside
Real-Time Search & Flexible Indexing DocValues aka. Column Stride Fields is one of the “next
generation” features. DocValues enable Lucene to efficiently store and retrieve type-safe Document
& Value pairs in a column stride fashion either entirely memory resident random access or disk
resident iterator based without the need to un-invert fields. Its final goal is to provide a
independently update-able per document storage for scoring, sorting or even filtering. This talk will
introduce the current state of development, implementation details, its features and how DocValues
have been integrated into Lucene’s Codec API for full extendability.
Lucene 4.0 is on its way to deliver a tremendous amount of new features and improvements. Beside Real-Time Search & Flexible Indexing DocValues aka. Column Stride Fields is one of the "next generation" features
Presented at a guest lecture at the Rijksuniversiteit Groningen as part of the web and cloud computing master course.
I presented a architecture for and working implementation of doing Hadoop based typeahead style search suggestions. There is a companion github repo with the code and config at: https://github.com/friso/rug (there's no documentation, though).
Quontra Solutions offers Job oriented Linux online training with updated technologies. For more info about our Linux online training contact us directly. We are providing Linux online training to all students throughout worldwide by real time faculties. Our Linux training strengthens your skills and knowledge which will helps you to gain a competitive advantage in starting your career. Outclasses will help you to gain knowledge on real time scenario. It will be most use full to boost up your career.
Our training sessions are designed in such a way that all the students can be convenient with the training schedules and course timings.
Along with Training, we also conduct several mock interviews along with Job Placement Assistance. Attend Free Demo before joining the class.
Our Features:
• Real world projects to get practical based experience
• Online tests to explore the resource learning
• Experienced certified trainers as instructors
• One to one personalized training with desktop access
• Case studies and state of art library to access study material
• Resume build assistance to win in interviews
Contact us:
Simson Andrew
Email: info@quontrasolutions.com
web: www.quontrasolutions.com
Quand Swift a été annoncé en 2014, personne n'aurait imaginé qu'un jour on aurait pu se servir de ce langage pour réaliser une application... côté serveur !
Ce talk a pour but de démontrer comment et dans quelle mesure nous pouvons écrire et deployer une application Web en utilisant Swift 3.0, en utilisant les plates-formes proposées par le marché. Nous découvrirons également les forces et faiblesses actuelles, les frameworks et les outils à utiliser aujourd'hui, ainsi que les extensions pour connecter notre application Swift côté serveur à des services tierces, tels que les systèmes de gestion de base de données.
Par Simone Civetta, Développeur iOS chez Xebia.
Introduction to DTrace (Dynamic Tracing), written by Brendan Gregg and delivered in 2007. While aimed at a Solaris-based audience, this introduction is still largely relevant today (2012). Since then, DTrace has appeared in other operating systems (Mac OS X, FreeBSD, and is being ported to Linux), and, many user-level providers have been developed to aid tracing of other languages.
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
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
2. Benefits
High-speed reads and writes of key/value pairs
sustained over growing volumes of data
Read costs are always 0 or 1 disk seek
Efficient use of memory
Simple file structures with strong durability
guarantees
3. Why “Lucene” KV store?
Uses Lucene’s “Directory” APIs for low-level file
access
Based on Lucene’s concepts of segment
files, soft deletes, background merges, commit
points etc BUT a fundamentally different form of
index
I’d like to offer it to the Lucene community as a
“contrib” module because they have a track
record in optimizing these same concepts (and
could potentially make use of it in Lucene?)
4. Example benchmark
results
Note, regular Lucene search indexes follow the same trajectory of the
“Common KV Store” when it comes to lookups on a store with millions
of keys
5. KV-Store High-level Design
Map Key hash (int) Disk pointer
(int)
held in
23434 0
RAM 6545463 10
874382 22
Num keys Key 1 Key 1 Value 1 Value 1 Key/values 2,3,4…
with hash size (byte [ ]) size (byte[ ])
(VInt) (VInt) (Vint)
Disk
1 3 Foo 3 Bar
2 5 Hello 5 World 7,Bonjour,8,Le Mon..
Most hashes have only one associated key and value Some hashes will
have key collisions
requiring the use of
extra columns here
6. Read logic (pseudo code)
int keyHash=hash(searchKey);
int filePointer=ramMap.get(keyHash); There is a
if filePointer is null guaranteed
maximum of one
return null for value; random disk seek
file.seek(filePointer); for any lookup
int numKeysWithHash=file.readInt()
for numKeysWithHash With a good
{ hashing function
most lookups will
storedKey=file.readKeyData(); only need to go
if(storedKey==searchKey) once around this
return file.readValueData(); loop
file.readValueData();
}
7. Write logic (pseudo code)
Updates will
int keyHash=hash(newKey); always append to
int oldFilePointer=ramMap.get(keyHash); the end of the
ramMap.put(keyHash,file.length()); file, leaving older
if oldFilePointer is null values
{ unreferenced
file.append(1);//only 1 key with hash
file.append(newKey);
file.append(newValue); In case of any key
}else collisions, previou
{ sly stored values
file.seek(oldFilePointer); are copied to the
int numOldKeys=file.readInt(); new position at
Map tmpMap=file.readNextNKeysAndValues(numOldKeys); the end of the file
tmpMap.put(newKey,newValue); along with the
file.append(tmpMap.size()); new content
file.appendKeysAndValues(tmpMap);
}
8. Segment generations:
writes
Hash Pointer Hash Pointer Hash Pointer Hash Pointer
Maps held 23434 0 203765 0 23434 0 15243 0
Writes append to
in RAM 65463 10 37594 10 65463 10
3 the end of the
74229 10 latest generation
… … … … … … 7
… …
segment until it
reaches a set
Key and size then it is
3
value disk made read-only
0 1 2
and new
stores
segment is
old created.
new
9. Segment generations:
reads
Maps held Hash Pointer Hash Pointer Hash Pointer Hash Pointer Read operations
23434 0 203765 0 23434 0 15243 0 search memory
in RAM 65463 10 37594 10 65463 10
3
74229 10
maps in reverse
… … … … … … 7 order. The first
… …
map found with a
hash is expected
Key and 3 to have a pointer
value disk 0 1 2 into its associated
stores file for all the latest
keys/values with
old new this hash
10. Segment generations:
merges
Hash Pointer Hash Pointer Hash Pointer Hash Pointer
Maps held 23434 0 20376 0 23434 0 15243 0
in RAM 65463 10
5
65463 10
3
37594 10 74229 10
… … … … 7
… …
… …
Key and 3
value disk 0 1 2
stores
A background thread
merges read-only
segments with many 4
outdated entries into
new, more compact
versions
11. Segment generations:
durability
Hash Pointer Hash Pointer Hash Pointer
Maps held 23434 0 203765 0 152433 0
in RAM 65463 10 37594 10 742297 10
… … … … … …
Key and 3
value disk 0 4
stores
Completed 0,4
Segment IDs
Active 3
Like Lucene, commit Segment ID
operations create a new Active 423423
segment
generation of a “segments” committed
length
file, the contents of which
reflect the committed (i.e.
fsync’ed state of the store.)
12. Implementation details
JVM needs sufficient RAM for 2 ints for every active key
(note: using “modulo N” on the hash can reduce RAM max
to Nx2 ints at the cost of more key collisions = more disk
IO)
Uses Lucene Directory for
Abstraction from choice of file system
Buffered reads/writes
Support for Vint encoding of numbers
Rate-limited merge operations
Borrows successful Lucene concepts:
Multiple segments flushed then made read-only.
“Segments” file used to list committed content (could
potentially support multiple commit points)
Background merges
Uses LGPL “Trove” for maps of primitives