By default PostgreSQL will store data on disk in its standard format. However, in many cases business or legal requirements require data to on disk be encrypted.
On disk-encryption was paid for by German industry and is a classical case showing how business and Open Source can coexist. This talk shows, that it can be actually cheaper to implement a feature into an Open Source product than to license a commercial product such as Oracle, DB2, Informix, or MS SQL Server.
Open Source can make a valuable contribution to save costs for everybody.
For the Docker users out there, Sematext's DevOps Evangelist, Stefan Thies, goes through a number of different Docker monitoring options, points out their pros and cons, and offers solutions for Docker monitoring. Webinar contains actionable content, diagrams and how-to steps.
PostgreSQL is one of the most advanced relational databases. It offers superb replication capabilities. The most important features are: Streaming replication, Point-In-Time-Recovery, advanced monitoring, etc.
By default PostgreSQL will store data on disk in its standard format. However, in many cases business or legal requirements require data to on disk be encrypted.
On disk-encryption was paid for by German industry and is a classical case showing how business and Open Source can coexist. This talk shows, that it can be actually cheaper to implement a feature into an Open Source product than to license a commercial product such as Oracle, DB2, Informix, or MS SQL Server.
Open Source can make a valuable contribution to save costs for everybody.
For the Docker users out there, Sematext's DevOps Evangelist, Stefan Thies, goes through a number of different Docker monitoring options, points out their pros and cons, and offers solutions for Docker monitoring. Webinar contains actionable content, diagrams and how-to steps.
PostgreSQL is one of the most advanced relational databases. It offers superb replication capabilities. The most important features are: Streaming replication, Point-In-Time-Recovery, advanced monitoring, etc.
This presentation is primarily focused on how to use collectd (http://collectd.org/) to gather data from the Postgres statistics tables. Examples of how to use collectd with Postgres will be shown. There is some hackery involved to make collectd do a little more and collect more meaningful data from Postgres. These small patches will be explored. A small portion of the discussion will be about how to visualize the data.
In recent years, many companies have adopted service-oriented architectures by deploying tens to hundreds of small microservices. But with the increasing number of independent services, do you still know what’s going on in your infrastructure?
Traditional monitoring solutions were mostly focused on machines and fell short keeping track of infrastructures where service deployments happen multiple times per day and instances get dynamically allocated on a multitude of nodes. Prometheus is a relatively new monitoring system which has gained a lot of popularity in the last two years as it was explicitly designed for today’s needs of service monitoring and container infrastructure.
In this session, you’ll learn how to instrument a service with a Prometheus client library to provide information about its current health and state. In order to get automatically notified when the service becomes unhealthy, you’ll see how to configure alerts and notifications. Along the way, I’ll discuss a few important key metrics paramount to successfully monitor a microservice.
A monitoring system is arguably the most crucial system to have in place when administering and tweaking the performance of any database system. DBAs also find themselves with a variety of monitoring systems and plugins to use; ranging from small scripts in cron to complex data collection systems. In this talk, I’ll discuss how Box made a shift from the Cacti monitoring system and other various shell scripts to OpenTSDB and the changes made to our servers and daily interaction with monitoring to increase our agility in identifying and addressing changes in database behavior.
WSO2 BAM 2.0 provide a rich set of tool for aggregation, analyzing and presentation for large scale data sets and any monitoring scenario can be easily modeled according to the BAM architecture. We selected WSO2 BAM as the backbone of our logging architecture with a Log4JAppender to send LogEvents to bam.
So, you’ve migrated your application to Reactive Microservices to get the last ounce of performance from your servers. Still want more? Perhaps you forgot about the logs: logs can be one of the few roadblocks on the road to ultimate performance. Everybody faces the same challenges: the application produces logs, and they need to be stored in our log storage – Elasticsearch, with the minimum of fuss and the fastest way possible. In this talk, I’ll show you some insider tips and tricks taken from my experience put you on the track toward fast(er) log management. Without revealing too much, it involves async loggers, JSON, Kafka and Logstash.
Monitoring an entire application is not a simple task, but with the right tools it is not a hard task either. However, events like Black Friday can push your application to the limit, and even cause crashes. As the system is stressed, it generates a lot more logs, which may crash the monitoring system as well. In this talk I will walk through the best practices when using the Elastic Stack to centralize and monitor your logs. I will also share some tricks to help you with the huge increase of traffic typical in Black Fridays.
Topics include:
monitoring architectures
optimal bulk size
distributing the load
cluster sizing
optimizing disk IO
monitoring queries
monitoring your monitoring system :P
Takeaway: best practices when building a monitoring system with the Elastic Stack, advanced tuning to optimize and increase event ingestion performance.
Infinum Android Talks #18 - In-app billing by Ivan MarićInfinum
You’re experienced in building Android apps but have trouble with the conversion rate? Ivan will talk about the monetization models Google Play offers and demonstrate how to use them in your apps.
Gradle is great for creating automated build tasks. We will explain why and how to code your own gradle plugins and make your build code reusable across projects.
This presentation is primarily focused on how to use collectd (http://collectd.org/) to gather data from the Postgres statistics tables. Examples of how to use collectd with Postgres will be shown. There is some hackery involved to make collectd do a little more and collect more meaningful data from Postgres. These small patches will be explored. A small portion of the discussion will be about how to visualize the data.
In recent years, many companies have adopted service-oriented architectures by deploying tens to hundreds of small microservices. But with the increasing number of independent services, do you still know what’s going on in your infrastructure?
Traditional monitoring solutions were mostly focused on machines and fell short keeping track of infrastructures where service deployments happen multiple times per day and instances get dynamically allocated on a multitude of nodes. Prometheus is a relatively new monitoring system which has gained a lot of popularity in the last two years as it was explicitly designed for today’s needs of service monitoring and container infrastructure.
In this session, you’ll learn how to instrument a service with a Prometheus client library to provide information about its current health and state. In order to get automatically notified when the service becomes unhealthy, you’ll see how to configure alerts and notifications. Along the way, I’ll discuss a few important key metrics paramount to successfully monitor a microservice.
A monitoring system is arguably the most crucial system to have in place when administering and tweaking the performance of any database system. DBAs also find themselves with a variety of monitoring systems and plugins to use; ranging from small scripts in cron to complex data collection systems. In this talk, I’ll discuss how Box made a shift from the Cacti monitoring system and other various shell scripts to OpenTSDB and the changes made to our servers and daily interaction with monitoring to increase our agility in identifying and addressing changes in database behavior.
WSO2 BAM 2.0 provide a rich set of tool for aggregation, analyzing and presentation for large scale data sets and any monitoring scenario can be easily modeled according to the BAM architecture. We selected WSO2 BAM as the backbone of our logging architecture with a Log4JAppender to send LogEvents to bam.
So, you’ve migrated your application to Reactive Microservices to get the last ounce of performance from your servers. Still want more? Perhaps you forgot about the logs: logs can be one of the few roadblocks on the road to ultimate performance. Everybody faces the same challenges: the application produces logs, and they need to be stored in our log storage – Elasticsearch, with the minimum of fuss and the fastest way possible. In this talk, I’ll show you some insider tips and tricks taken from my experience put you on the track toward fast(er) log management. Without revealing too much, it involves async loggers, JSON, Kafka and Logstash.
Monitoring an entire application is not a simple task, but with the right tools it is not a hard task either. However, events like Black Friday can push your application to the limit, and even cause crashes. As the system is stressed, it generates a lot more logs, which may crash the monitoring system as well. In this talk I will walk through the best practices when using the Elastic Stack to centralize and monitor your logs. I will also share some tricks to help you with the huge increase of traffic typical in Black Fridays.
Topics include:
monitoring architectures
optimal bulk size
distributing the load
cluster sizing
optimizing disk IO
monitoring queries
monitoring your monitoring system :P
Takeaway: best practices when building a monitoring system with the Elastic Stack, advanced tuning to optimize and increase event ingestion performance.
Infinum Android Talks #18 - In-app billing by Ivan MarićInfinum
You’re experienced in building Android apps but have trouble with the conversion rate? Ivan will talk about the monetization models Google Play offers and demonstrate how to use them in your apps.
Gradle is great for creating automated build tasks. We will explain why and how to code your own gradle plugins and make your build code reusable across projects.
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Infinum Android Talks #14 - How (not) to get f***** by checkstyle, pdm, findb...Infinum
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Building scalable, highly-available applications that perform well is not an easy task. These features cannot be simply “bolted” onto an existing application – they have to be architected into it. Unfortunately, the things we need to do to achieve them are often in conflict with each other, and finding the right balance is crucial. In this session we will discuss why scaling web applications is difficult and will look at some of solutions we have come up with in the past to deal with the issues involved. We will then look at how in-memory data grids can make our jobs easier by providing a solid architectural foundation to build our applications on top of. If you are new to in-memory data grids, you are guaranteed to leave the presentation eager to learn more. However, even if you are already using one you will likely walk out with a few ideas on how to improve performance and scalability of your applications.
Java In-Process Caching - Performance, Progress and PitfallsJens Wilke
How to speed up in-process caching and why you should not use LRU. Comparison of EHCache, Google Guava, Caffeine and cache2k. With benchmarks of throughput and eviction efficiency.
Java In-Process Caching - Performance, Progress and Pittfallscruftex
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This talk presents how we accelerated deep learning processing from preprocessing to inference and training on Apache Spark in SK Telecom. In SK Telecom, we have half a Korean population as our customers. To support them, we have 400,000 cell towers, which generates logs with geospatial tags.
Drizzle—Low Latency Execution for Apache Spark: Spark Summit East talk by Shi...Spark Summit
Drizzle is a low latency execution engine for Apache Spark
that is targeted at stream processing and iterative workloads.
Currently, Spark uses a BSP computation model, and notifies the
scheduler at the end of each task. Invoking the scheduler at the end of each task adds overheads and results in decreased throughput and increased latency. In Drizzle, we introduce group scheduling, where multiple batches (or a group) of computation are scheduled at once.
This helps decouple the granularity of task execution from scheduling and amortize the costs of task serialization and launch. Our experiments on a 128 node EC2 cluster show that Drizzle can achieve end-to-end streaming latencies of less than 100ms and can get up to 3.5x lower latency than Spark Streaming. Compared to Apache Flink, a record-at-a-time streaming system, we show that Drizzle can recover around 4x faster from failures and that Drizzle has up to 13x lower latency during recovery.
Everything you wanted to know about writing async, concurrent http apps in java Baruch Sadogursky
As presented at CodeMotion Tel Aviv:
Facing tens of millions of clients continuously downloading binaries from its repositories, JFrog decided to offer an OSS client that natively supports these downloads. This session shares the main challenges of developing a highly concurrent, resumable, async download library on top of an Apache HTTP client. It also covers other libraries JFrog tested and why it decided to reinvent the wheel. Consider yourself forewarned: lots of HTTP internals, NIO, and concurrency ahead!
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A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
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OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
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Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
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👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
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✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
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See My Other Reviews Article:
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This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
8. Cache network responses on OkHttp client.
Custom cache size and cache time validity.
No support for data caching, which is created in your app.
OKHTTP RESPONSE CACHING
9. private static final Interceptor cacheControlInterceptor = new Interceptor() {
@Override
public Response intercept(Chain chain) throws IOException {
Request request = chain.request();
Response response = chain.proceed(request);
if (shouldCacheResponse(request)) {
// Re-write response CC header to force use of cache
return response.newBuilder()
.header("Cache-Control", "public, max-age=3600") // 1 hour
.build();
} else {
return response;
}
}
};
10. Simple POJO object which is used for data storage.
Singleton, provided by AppComponent.
Can have shorter lifecycle than app lifecycle.
Used for storing sensitive user data.
SESSION OBJECT
11. If you need to store it, the data should be encrypted.
SESSION OBJECT
12. Memory cache which holds strong references to a limited
number of values.
It has a limited space requirement.
Deleted on app restart.
LRU CACHE
13. Each time a valued is accessed, it is moved to the head of the
queue.
When a value is put into the cache, the value at the end of the
list may be evicted.
LRU CACHE - HOW IT WORKS
14. Similar to LruCache, but the data is stored in a directory on a
filesystem.
Use Jake Wharton’s implementation.
DISK LRU CACHE
15. For storing primitive data in key-value pairs.
Can be private or shared.
Stored on filesystem.
PREFERENCES
16. Native support for relational databases (SQLite).
Support for NoSQL databases by 3rd party libraries (Realm,
Firebase, CouchBaseLite)
DATABASES
17. Full database support
• transactions
• database schemas
• CRUD actions
• migrations
• can be copied, deleted, exported
DATABASES
18.
19.
20.
21. Thank you!
Visit www.infinum.co or find us on social networks:
infinum.co infinumco infinumco infinum
@ZELJKOPLESAC
ZELJKO.PLESAC@INFINUM.CO