PHP site optimization can improve performance by 10-15% through techniques like using Nginx + APC + caching + Memcache + CDN. Database performance is key and can be optimized through simplifying queries, optimization, replication and sharding. Network latency issues can be identified through slow page responses and CPU usage, and addressed through techniques like persistent connections and reducing the number of hops. IO contention and high CPU utilization should also be addressed.
Project Tungsten: Bringing Spark Closer to Bare MetalDatabricks
As part of the Tungsten project, Spark has started an ongoing effort to dramatically improve performance to bring the execution closer to bare metal. In this talk, we’ll go over the progress that has been made so far and the areas we’re looking to invest in next. This talk will discuss the architectural changes that are being made as well as some discussion into how Spark users can expect their application to benefit from this effort. The focus of the talk will be on Spark SQL but the improvements are general and applicable to multiple Spark technologies.
Debugging PySpark: Spark Summit East talk by Holden KarauSpark Summit
Apache Spark is one of the most popular big data projects, offering greatly improved performance over traditional MapReduce models. Much of Apache Spark’s power comes from lazy evaluation along with intelligent pipelining, which can make debugging more challenging. This talk will examine how to debug Apache Spark applications, the different options for logging in Spark’s variety of supported languages, as well as some common errors and how to detect them.
Spark’s own internal logging can often be quite verbose, and this talk will examine how to effectively search logs from Apache Spark to spot common problems. In addition to the internal logging, this talk will look at options for logging from within our program itself.
Spark’s accumulators have gotten a bad rap because of how they interact in the event of cache misses or partial recomputes, but this talk will look at how to effectively use Spark’s current accumulators for debugging as well as a look to future for data property type accumulators which may be coming to Spark in future version.
In addition to reading logs, and instrumenting our program with accumulators, Spark’s UI can be of great help for quickly detecting certain types of problems.
Prediction as a service with ensemble model in SparkML and Python ScikitLearnJosef A. Habdank
Watch the recording of the speech done at Spark Summit Brussles 2016 here:
https://www.youtube.com/watch?v=wyfTjd9z1sY
Data Science with SparkML on DataBricks is a perfect platform for application of Ensemble Learning on massive a scale. This presentation describes Prediction-as-a-Service platform which can predict trends on 1 billion observed prices daily. In order to train ensemble model on a multivariate time series in thousands/millions dimensional space, one has to fragment the whole space into subspaces which exhibit a significant similarity. In order to achieve this, the vastly sparse space has to undergo dimensionality reduction into a parameters space which then is used to cluster the observations. The data in the resulting clusters is modeled in parallel using machine learning tools capable of coefficient estimation at the massive scale (SparkML and Scikit Learn). The estimated model coefficients are stored in a database to be used when executing predictions on demand via a web service. This approach enables training models fast enough to complete the task within a couple of hours, allowing daily or even real time updates of the coefficients. The above machine learning framework is used to predict the airfares used as support tool for the airline Revenue Management systems.
Paul Tarjan ( http://github.com/ptarjan ) presented this to the Hadoop User Group at the Yahoo! Sunnyvale campus on 11/18/09. Paul describes his solution for building a Hadoop Record Reader in Python.
SparkR - Play Spark Using R (20160909 HadoopCon)wqchen
1. Introduction to SparkR
2. Demo
Starting to use SparkR
DataFrames: dplyr style, SQL style
RDD v.s. DataFrames
SparkR on MLlib: GLM, K-means
3. User Case
Median: approxQuantile()
ID Match: dplyr style, SQL style, SparkR function
SparkR + Shiny
4. The Future of SparkR
Project Tungsten: Bringing Spark Closer to Bare MetalDatabricks
As part of the Tungsten project, Spark has started an ongoing effort to dramatically improve performance to bring the execution closer to bare metal. In this talk, we’ll go over the progress that has been made so far and the areas we’re looking to invest in next. This talk will discuss the architectural changes that are being made as well as some discussion into how Spark users can expect their application to benefit from this effort. The focus of the talk will be on Spark SQL but the improvements are general and applicable to multiple Spark technologies.
Debugging PySpark: Spark Summit East talk by Holden KarauSpark Summit
Apache Spark is one of the most popular big data projects, offering greatly improved performance over traditional MapReduce models. Much of Apache Spark’s power comes from lazy evaluation along with intelligent pipelining, which can make debugging more challenging. This talk will examine how to debug Apache Spark applications, the different options for logging in Spark’s variety of supported languages, as well as some common errors and how to detect them.
Spark’s own internal logging can often be quite verbose, and this talk will examine how to effectively search logs from Apache Spark to spot common problems. In addition to the internal logging, this talk will look at options for logging from within our program itself.
Spark’s accumulators have gotten a bad rap because of how they interact in the event of cache misses or partial recomputes, but this talk will look at how to effectively use Spark’s current accumulators for debugging as well as a look to future for data property type accumulators which may be coming to Spark in future version.
In addition to reading logs, and instrumenting our program with accumulators, Spark’s UI can be of great help for quickly detecting certain types of problems.
Prediction as a service with ensemble model in SparkML and Python ScikitLearnJosef A. Habdank
Watch the recording of the speech done at Spark Summit Brussles 2016 here:
https://www.youtube.com/watch?v=wyfTjd9z1sY
Data Science with SparkML on DataBricks is a perfect platform for application of Ensemble Learning on massive a scale. This presentation describes Prediction-as-a-Service platform which can predict trends on 1 billion observed prices daily. In order to train ensemble model on a multivariate time series in thousands/millions dimensional space, one has to fragment the whole space into subspaces which exhibit a significant similarity. In order to achieve this, the vastly sparse space has to undergo dimensionality reduction into a parameters space which then is used to cluster the observations. The data in the resulting clusters is modeled in parallel using machine learning tools capable of coefficient estimation at the massive scale (SparkML and Scikit Learn). The estimated model coefficients are stored in a database to be used when executing predictions on demand via a web service. This approach enables training models fast enough to complete the task within a couple of hours, allowing daily or even real time updates of the coefficients. The above machine learning framework is used to predict the airfares used as support tool for the airline Revenue Management systems.
Paul Tarjan ( http://github.com/ptarjan ) presented this to the Hadoop User Group at the Yahoo! Sunnyvale campus on 11/18/09. Paul describes his solution for building a Hadoop Record Reader in Python.
SparkR - Play Spark Using R (20160909 HadoopCon)wqchen
1. Introduction to SparkR
2. Demo
Starting to use SparkR
DataFrames: dplyr style, SQL style
RDD v.s. DataFrames
SparkR on MLlib: GLM, K-means
3. User Case
Median: approxQuantile()
ID Match: dplyr style, SQL style, SparkR function
SparkR + Shiny
4. The Future of SparkR
Apache Spark™ is a fast and general engine for large-scale data processing. Spark is written in Scala and runs on top of JVM, but Python is one of the officially supported languages. But how does it actually work? How can Python communicate with Java / Scala? In this talk, we’ll dive into the PySpark internals and try to understand how to write and test high-performance PySpark applications.
London Spark Meetup Project Tungsten Oct 12 2015Chris Fregly
Building on a previous talk about how Spark beat Hadoop @ 100TB Daytona GraySort, we present low-level details of Project Tungsten which includes many CPU and Memory optimizations.
H2O World - Munging, modeling, and pipelines using Python - Hank RoarkSri Ambati
H2O World 2015 - Hank Roark
Hank's iPython Notebook for this presentation can be found here: https://github.com/h2oai/h2o-world-2015-training/blob/master/tutorials/python-munging-modeling-pipelines/Munging-Modeling-Pipelines-Using-H2O-Pipelines.ipynb
Back in ye olde days of Spark, using Python with Spark was an exercise in patience. Data was moving up and down from Python to Scala, being serialised constantly. Leveraging SparkSQL and avoiding UDFs made things better, as well as the constant improvement of the optimisers (Catalyst and Tungsten). But, with Spark 2.3 PySpark has speed up tremendously thanks to the (still experimental) addition of the Arrow serialisers.
In this talk we will learn how PySpark has improved its performance in Apache Spark 2.3 by using Apache Arrow. To do this, we will travel through the internals of Spark to find how Python interacts with the Scala core, and some of the internals of Pandas to see how data moves from Python to Scala via Arrow.
https://github.com/rberenguel/pyspark-arrow-pandas
From HelloWorld to Configurable and Reusable Apache Spark Applications in Sca...Databricks
We can think of an Apache Spark application as the unit of work in complex data workflows. Building a configurable and reusable Apache Spark application comes with its own challenges, especially for developers that are just starting in the domain. Configuration, parametrization, and reusability of the application code can be challenging. Solving these will allow the developer to focus on value-adding work instead of mundane tasks such as writing a lot of configuration code, initializing the SparkSession or even kicking-off a new project.
This presentation will describe using code samples a developer’s journey from the first steps into Apache Spark all the way to a simple open-source framework that can help kick-off an Apache Spark project very easy, with a minimal amount of code. The main ideas covered in this presentation are derived from the separation of concerns principle.
The first idea is to make it even easier to code and test new Apache Spark applications by separating the application logic from the configuration logic.
The second idea is to make it easy to configure the applications, providing SparkSessions out-of-the-box, easy to set-up data readers, data writers and application parameters through configuration alone.
The third idea is that taking a new project off the ground should be very easy and straightforward. These three ideas are a good start in building reusable and production-worthy Apache Spark applications.
The resulting framework, spark-utils, is already available and ready to use as an open-source project, but even more important are the ideas and principles behind it.
This presentation was used by Blair during his talk on Aurora and PostgreSQl compatibility for Aurora at pgDay Asia 2017. The talk was part of dedicated PostgreSQL track at FOSSASIA 2017
Fine Tuning and Enhancing Performance of Apache Spark JobsDatabricks
Apache Spark defaults provide decent performance for large data sets but leave room for significant performance gains if able to tune parameters based on resources and job.
Scalable Data Science with SparkR: Spark Summit East talk by Felix CheungSpark Summit
R is a very popular platform for Data Science. Apache Spark is a highly scalable data platform. How could we have the best of both worlds? How could a Data Scientist leverage the rich 9000+ packages on CRAN, and integrate Spark into their existing Data Science toolset?
In this talk we will walkthrough many examples how several new features in Apache Spark 2.x will enable this. We will also look at exciting changes in and coming next in Apache Spark 2.x releases.
Tuning Apache Spark for Large-Scale Workloads Gaoxiang Liu and Sital KediaDatabricks
Apache Spark is a fast and flexible compute engine for a variety of diverse workloads. Optimizing performance for different applications often requires an understanding of Spark internals and can be challenging for Spark application developers. In this session, learn how Facebook tunes Spark to run large-scale workloads reliably and efficiently. The speakers will begin by explaining the various tools and techniques they use to discover performance bottlenecks in Spark jobs. Next, you’ll hear about important configuration parameters and their experiments tuning these parameters on large-scale production workload. You’ll also learn about Facebook’s new efforts towards automatically tuning several important configurations based on nature of the workload. The speakers will conclude by sharing their results with automatic tuning and future directions for the project.ing several important configurations based on nature of the workload. We will conclude by sharing our result with automatic tuning and future directions for the project.
(BDT208) A Technical Introduction to Amazon Elastic MapReduceAmazon Web Services
"Amazon EMR provides a managed framework which makes it easy, cost effective, and secure to run data processing frameworks such as Apache Hadoop, Apache Spark, and Presto on AWS. In this session, you learn the key design principles behind running these frameworks on the cloud and the feature set that Amazon EMR offers. We discuss the benefits of decoupling compute and storage and strategies to take advantage of the scale and the parallelism that the cloud offers, while lowering costs. Additionally, you hear from AOL’s Senior Software Engineer on how they used these strategies to migrate their Hadoop workloads to the AWS cloud and lessons learned along the way.
In this session, you learn the benefits of decoupling storage and compute and allowing them to scale independently; how to run Hadoop, Spark, Presto and other supported Hadoop Applications on Amazon EMR; how to use Amazon S3 as a persistent data-store and process data directly from Amazon S3; dDeployment strategies and how to avoid common mistakes when deploying at scale; and how to use Spot instances to scale your transient infrastructure effectively."
Apache Spark™ is a fast and general engine for large-scale data processing. Spark is written in Scala and runs on top of JVM, but Python is one of the officially supported languages. But how does it actually work? How can Python communicate with Java / Scala? In this talk, we’ll dive into the PySpark internals and try to understand how to write and test high-performance PySpark applications.
London Spark Meetup Project Tungsten Oct 12 2015Chris Fregly
Building on a previous talk about how Spark beat Hadoop @ 100TB Daytona GraySort, we present low-level details of Project Tungsten which includes many CPU and Memory optimizations.
H2O World - Munging, modeling, and pipelines using Python - Hank RoarkSri Ambati
H2O World 2015 - Hank Roark
Hank's iPython Notebook for this presentation can be found here: https://github.com/h2oai/h2o-world-2015-training/blob/master/tutorials/python-munging-modeling-pipelines/Munging-Modeling-Pipelines-Using-H2O-Pipelines.ipynb
Back in ye olde days of Spark, using Python with Spark was an exercise in patience. Data was moving up and down from Python to Scala, being serialised constantly. Leveraging SparkSQL and avoiding UDFs made things better, as well as the constant improvement of the optimisers (Catalyst and Tungsten). But, with Spark 2.3 PySpark has speed up tremendously thanks to the (still experimental) addition of the Arrow serialisers.
In this talk we will learn how PySpark has improved its performance in Apache Spark 2.3 by using Apache Arrow. To do this, we will travel through the internals of Spark to find how Python interacts with the Scala core, and some of the internals of Pandas to see how data moves from Python to Scala via Arrow.
https://github.com/rberenguel/pyspark-arrow-pandas
From HelloWorld to Configurable and Reusable Apache Spark Applications in Sca...Databricks
We can think of an Apache Spark application as the unit of work in complex data workflows. Building a configurable and reusable Apache Spark application comes with its own challenges, especially for developers that are just starting in the domain. Configuration, parametrization, and reusability of the application code can be challenging. Solving these will allow the developer to focus on value-adding work instead of mundane tasks such as writing a lot of configuration code, initializing the SparkSession or even kicking-off a new project.
This presentation will describe using code samples a developer’s journey from the first steps into Apache Spark all the way to a simple open-source framework that can help kick-off an Apache Spark project very easy, with a minimal amount of code. The main ideas covered in this presentation are derived from the separation of concerns principle.
The first idea is to make it even easier to code and test new Apache Spark applications by separating the application logic from the configuration logic.
The second idea is to make it easy to configure the applications, providing SparkSessions out-of-the-box, easy to set-up data readers, data writers and application parameters through configuration alone.
The third idea is that taking a new project off the ground should be very easy and straightforward. These three ideas are a good start in building reusable and production-worthy Apache Spark applications.
The resulting framework, spark-utils, is already available and ready to use as an open-source project, but even more important are the ideas and principles behind it.
This presentation was used by Blair during his talk on Aurora and PostgreSQl compatibility for Aurora at pgDay Asia 2017. The talk was part of dedicated PostgreSQL track at FOSSASIA 2017
Fine Tuning and Enhancing Performance of Apache Spark JobsDatabricks
Apache Spark defaults provide decent performance for large data sets but leave room for significant performance gains if able to tune parameters based on resources and job.
Scalable Data Science with SparkR: Spark Summit East talk by Felix CheungSpark Summit
R is a very popular platform for Data Science. Apache Spark is a highly scalable data platform. How could we have the best of both worlds? How could a Data Scientist leverage the rich 9000+ packages on CRAN, and integrate Spark into their existing Data Science toolset?
In this talk we will walkthrough many examples how several new features in Apache Spark 2.x will enable this. We will also look at exciting changes in and coming next in Apache Spark 2.x releases.
Tuning Apache Spark for Large-Scale Workloads Gaoxiang Liu and Sital KediaDatabricks
Apache Spark is a fast and flexible compute engine for a variety of diverse workloads. Optimizing performance for different applications often requires an understanding of Spark internals and can be challenging for Spark application developers. In this session, learn how Facebook tunes Spark to run large-scale workloads reliably and efficiently. The speakers will begin by explaining the various tools and techniques they use to discover performance bottlenecks in Spark jobs. Next, you’ll hear about important configuration parameters and their experiments tuning these parameters on large-scale production workload. You’ll also learn about Facebook’s new efforts towards automatically tuning several important configurations based on nature of the workload. The speakers will conclude by sharing their results with automatic tuning and future directions for the project.ing several important configurations based on nature of the workload. We will conclude by sharing our result with automatic tuning and future directions for the project.
(BDT208) A Technical Introduction to Amazon Elastic MapReduceAmazon Web Services
"Amazon EMR provides a managed framework which makes it easy, cost effective, and secure to run data processing frameworks such as Apache Hadoop, Apache Spark, and Presto on AWS. In this session, you learn the key design principles behind running these frameworks on the cloud and the feature set that Amazon EMR offers. We discuss the benefits of decoupling compute and storage and strategies to take advantage of the scale and the parallelism that the cloud offers, while lowering costs. Additionally, you hear from AOL’s Senior Software Engineer on how they used these strategies to migrate their Hadoop workloads to the AWS cloud and lessons learned along the way.
In this session, you learn the benefits of decoupling storage and compute and allowing them to scale independently; how to run Hadoop, Spark, Presto and other supported Hadoop Applications on Amazon EMR; how to use Amazon S3 as a persistent data-store and process data directly from Amazon S3; dDeployment strategies and how to avoid common mistakes when deploying at scale; and how to use Spot instances to scale your transient infrastructure effectively."
A powerful ORM tool to design data base access layer.
Fills gaps of mismatches between OOPs and RDBMS paradigm. Also maintains adequate ultra performance of database access.
An automated, configurable persistence of java objects with tables in data base.
May not be a good solution for data-centric application which uses only stored procedures to implement business logic in the database.
Introduction to Apache Flink - Fast and reliable big data processingTill Rohrmann
This presentation introduces Apache Flink, a massively parallel data processing engine which currently undergoes the incubation process at the Apache Software Foundation. Flink's programming primitives are presented and it is shown how easily a distributed PageRank algorithm can be implemented with Flink. Intriguing features such as dedicated memory management, Hadoop compatibility, streaming and automatic optimisation make it an unique system in the world of Big Data processing.
Emerging technologies /frameworks in Big DataRahul Jain
A short overview presentation on Emerging technologies /frameworks in Big Data covering Apache Parquet, Apache Flink, Apache Drill with basic concepts of Columnar Storage and Dremel.
This is a slide deck that was used for our 11/19/15 Nike Tech Talk to give a detailed overview of the SnappyData technology vision. The slides were presented by Jags Ramnarayan, Co-Founder & CTO of SnappyData
With the advent of Hadoop, there comes the need for professionals skilled in Hadoop Administration making it imperative to be skilled as a Hadoop Admin for better career, salary and job opportunities.
Ingesting Over Four Million Rows Per Second With QuestDB Timeseries Database ...javier ramirez
How would you build a database to support sustained ingestion of several hundreds of thousands rows per second while running near real-time queries on top?
In this session I will go over some of the technical decisions and trade-offs we applied when building QuestDB, an open source time-series database developed mainly in JAVA, and how we can achieve over four million row writes per second on a single instance without blocking or slowing down the reads. There will be code and demos, of course.
We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
SenchaCon 2016: LinkRest - Modern RESTful API Framework for Ext JS Apps - Rou...Sencha
LinkRest is an active project commissioned and open sourced by NHL under Apache 2.0 license. It was conceived specifically as the server-side counterpart to Ext JS, so it natively supports Ext JS rest proxy, including CRUD, grouping, sorting, filtering, and more. In this session, we'll review Ext JS protocol support. You'll also see extensions that allow the client to safely query the server for specific data and shape the response. We'll cover advanced features, including security, constraints, idempotent methods, and metadata service. We'll show demos and try to allow time for Q&A.
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.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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.
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.
3. Performance or Scalability. Both?
Hiphop (Facebook implementation of php) is high
performing but not scalable
4. Workload distribution of a page
request
10% web server
40% php processing
50% database response
10-15% improvement easily achievable using a
commonly used alternative stack i.e. Nginx +
APC + Htmlcaching + Memcache + CDN
6. Network Latency
How to identify?
When both page response and CPU usage are
slow
Reasons?
DNS Reverse Lookups, TCP Handshakes, High
number of hops
Tools?
Tcpdump, ping, traceroute ..
13. ORM options
Active Record (made popular by Ruby on Rails)
Data Mapper
Collection
Doctrine
Data access methods used by Yii, CI,
Symphony ...
14. Active Record (AR)
It is an object-relational mapping and object-
persistence pattern
It binds a business object with a relational record
(row)
AR class maps to a db table (or view)
AR instance maps to a db table record
AR instance properties maps to db table record
fields
Instance methods act on a specific record
18. Preg
Stripos is 2 times faster than preg
ctype_alnum is 5 times faster than preg
Casting “if ($var == (int) $var)” is 5 times faster
than preg_match(“/^d*$/”, $var)
19. Magic methods
__get, __set, __call
Used by Soap, data tables, java objects
Use sparingly and avoid too much recursion
21. Queue
Queueing is offloading long running tasks to
queuing system
Job Queue
Gearman
Message Queue
ActiveMQ
AWS Simple Queue Service
22. What not to do
Caching should be the last thing to do. (Focus on
performance optimization)
Avoid LIKE queries. (Try Solr, Zend_Lucene)
23. What to do
Minimize require_once. Use autoloader
Horizontal architecture is better than vertical
ex. wide inheritance is better than deep level
inheritance
Lazy loading / Load on Demand
Use diagnostic tools
Zend studio profiler, Code Tracing