MongoDB Offers Field Level Encryption

Post Syndicated from Bruce Schneier original

MongoDB now has the ability to encrypt data by field:

MongoDB calls the new feature Field Level Encryption. It works kind of like end-to-end encrypted messaging, which scrambles data as it moves across the internet, revealing it only to the sender and the recipient. In such a “client-side” encryption scheme, databases utilizing Field Level Encryption will not only require a system login, but will additionally require specific keys to process and decrypt specific chunks of data locally on a user’s device as needed. That means MongoDB itself and cloud providers won’t be able to access customer data, and a database’s administrators or remote managers don’t need to have access to everything either.

For regular users, not much will be visibly different. If their credentials are stolen and they aren’t using multifactor authentication, an attacker will still be able to access everything the victim could. But the new feature is meant to eliminate single points of failure. With Field Level Encryption in place, a hacker who steals an administrative username and password, or finds a software vulnerability that gives them system access, still won’t be able to use these holes to access readable data.

Raspberry Pi 4: 48 hours later

Post Syndicated from Alex Bate original

“We’ve never felt more betrayed and excited at the same time,” admitted YouTubers 8 Bits and a Byte when I told them Raspberry Pi 4 would be out in June, going against rumours of the release happening at some point in 2020. Fortunately, everything worked in our favour, and we were able to get our new product out ahead of schedule.

So, while we calm down from the hype of Monday, here’s some great third-party content for you to get your teeth into.


A select few online content creators were allowed to get their hands on Raspberry Pi 4 before its release date, and they published some rather wonderful videos on the big day.

Office favourite Explaining Computers provided viewers with a brilliant explanation of the ins and outs of Raspberry Pi 4, and even broke their usually Sunday-only release schedule to get the video out to fans for launch day. Thanks, Chris!

Raspberry Pi 4 Model B

Raspberry Pi 4B review, including the hardware specs of this new single board computer, and a demo running the latest version of Raspbian. With thanks to the Raspberry Pi Foundation for supplying the board featured in this video.

Blitz City DIY offered viewers a great benchmark test breakdown, delving deeper into the numbers and what they mean, to show the power increase compared to Raspberry Pi 3B+.

A Wild Raspberry Pi 4 Appears: Hardware Specs, Benchmarks & First Impressions

The Raspberry Pi 4 B has been released into the wild much earlier than anticipated. I was able to receive a review sample so here are the hardware specs, some benchmarks comparing it to the Pi 3 B and Pi 3 B+ and finally some first impressions.

Curious about how these creators were able to get their hands on Raspberry Pi 4 prior to its release? This is legitimately how Estefannie bagged herself the computer pre-launch. Honest.


I needed a new Raspberry Pi. FIND ME HERE: * * * * *

For their launch day video, Dane and Nicole, AKA 8 Bits and a Byte, built a pi-calculating pie that prints pies using a Raspberry Pi 4. Delicious.

The new Raspberry Pi 4 – Highlights & Celebration Project!

There’s a new Raspberry Pi, the Raspberry Pi 4! We give you a quick overview and build a project to welcome the Raspberry Pi 4 to the world!

Alex from Low Spec Gamer took his Raspberry Pi 4 home with him after visiting the office to talk to Eben. Annoyingly, I was away on vacation and didn’t get to meet him 🙁

Raspberry Pi 4 Hands-on. I got an early unit!

Watch the best documentaries on Curiosity Stream: #RaspberryPi4 #HandsOn #Preview A new Raspberry Pi joins the fray. I got an early Raspberry Pi 4 and decided to explore some of its differences with Eben Upton, founder of Raspberry Pi. All benchmarks run on an early version of the new raspbian.

The MagPi magazine managed to collar Raspberry Pi Trading’s COO James Adams for their video, filmed at the Raspberry Pi Store in Cambridge.

Introducing Raspberry Pi 4! + interview with a Raspberry Pi engineer

The brand new Raspberry Pi 4 is here! With up to 4GB of RAM, 4K HDMI video, Gigabit Ethernet, USB 3.0, and USB C, it is the ultimate Raspberry Pi. We talk to Raspberry Pi hardware lead James Adams about its amazing performance.

Some rather lovely articles

If you’re looking to read more about Raspberry Pi 4 and don’t know where to start, here are a few tasty treats to get you going:

Raspberry Pi 4 isn’t the only new thing to arrive this week. Raspbian Buster is now available for Raspberry Pi, and you can read more about it here.

Join the Raspberry Pi 4 conversation by using #RaspberryPi4 across all social platforms, and let us know what you plan to do with your new Raspberry Pi.

The post Raspberry Pi 4: 48 hours later appeared first on Raspberry Pi.

Confirmed: Supremacy Kodi Repo Was Indeed Targeted By Police

Post Syndicated from Andy original

On June 13, 2019, the Covert Development and Disruption Team of the UK’s North West Regional Organised Crime Unit arrested an individual said to be responsible for an allegedly-infringing Kodi add-on.

The unit revealed that the 40-year-old man was detained in Winsford, Cheshire, following an investigation in cooperation with the Federation Against Copyright Theft. The add-on was unnamed but was reportedly configured to supply illegal online streams.

When TorrentFreak tried to fill in the gaps, considerable circumstantial evidence pointed to the likelihood that the arrested man was connected to the Supremacy add-on repository. Today we are in a position to confirm that belief following discussion with FACT director general Kieron Sharp.

Since there are limitations on what can be discussed when a case is ongoing, we asked Sharp why the matter had been referred to the authorities. There have been numerous instances of add-on developers in the UK being served with private cease-and-desist notices so why was this case different and why did it warrant an organized crime unit getting involved?

“This was a decision taken by FACT who advised rights holders such as PL [Premier League], Sky, BT Sport and VM [Virgin Media] that police action was the most proportionate response to the level of damage and harm that was being caused by these entities,” Sharp explains.

“Other industry groups have used different tactics which are reasonable in certain circumstances, but FACT have the partnerships in LEA’s [law enforcement agencies] to enable this type of action to be considered.”

Sharp says that when FACT presented its evidence to the police, they considered the case serious enough to take action, which resulted in the individual operating as ‘Supremacy’ being arrested.

FACT’s director general rejects the notion that handing a case over to the police is the easy option, insisting that a referral to the authorities requires that an investigation takes place to particular standards.

“To get any LEA to act in these matters requires a high level of evidence. Given the pressure on LEA resources and many other priorities, FACT are very careful in which cases they will approach LEA’s with and have many other strategies for disrupting illegal activity which are used constantly,” Sharp says.

In the wake of the arrest, several other Kodi add-on repositories shut down, presumably due to fears of similar action. This hasn’t gone unnoticed by FACT, with Sharp noting that several strategies to disrupt piracy are deployed with the results taken on board.

“[I]t would appear, from their own comments, that the action has panicked the others. This is not uncommon but more often seen after a criminal conviction. It shows that action needs to be taken and that it can have an impact on the piracy problem. There is no one solution so a range of tactics have to be tried and implemented and the outcomes monitored,” Sharp concludes.

There can be little doubt that the involvement of the police in the shutdown of a Kodi repository and associated add-ons is somewhat of a game-changer in the UK. Where once a sternly-worded letter may have been a warning sign, there is now a worrying precedent for those engaged in similar activity.

What the final charges will be in this case, if any, remain unclear. However, FACT has a history of pursuing convictions under the Fraud Act, which can carry harsher sentences than those actioned under copyright law.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN reviews, discounts, offers and coupons.

How to Design Your Serverless Apps for Massive Scale

Post Syndicated from George Mao original

Serverless is one of the hottest design patterns in the cloud today, allowing you to focus on building and innovating, rather than worrying about the heavy lifting of server and OS operations. In this series of posts, we’ll discuss topics that you should consider when designing your serverless architectures. First, we’ll look at architectural patterns designed to achieve massive scale with serverless.

Scaling Considerations

In general, developers in a “serverful” world need to be worried about how many total requests can be served throughout the day, week, or month, and how quickly their system can scale. As you move into the serverless world, the most important question you should understand becomes: “What is the concurrency that your system is designed to handle?”

The AWS Serverless platform allows you to scale very quickly in response to demand. Below is an example of a serverless design that is fully synchronous throughout the application. During periods of extremely high demand, Amazon API Gateway and AWS Lambda will scale in response to your incoming load. This design places extremely high load on your backend relational database because Lambda can easily scale from thousands to tens of thousands of concurrent requests. In most cases, your relational databases are not designed to accept the same number of concurrent connections.

Serverless at scale-1

This design risks bottlenecks at your relational database and may cause service outages. This design also risks data loss due to throttling or database connection exhaustion.

Cloud Native Design

Instead, you should consider decoupling your architecture and moving to an asynchronous model. In this architecture, you use an intermediary service to buffer incoming requests, such as Amazon Kinesis or Amazon Simple Queue Service (SQS). You can configure Kinesis or SQS as out of the box event sources for Lambda. In design below, AWS will automatically poll your Kinesis stream or SQS resource for new records and deliver them to your Lambda functions. You can control the batch size per delivery and further place throttles on a per Lambda function basis.

Serverless at scale - 2

This design allows you to accept extremely high volume of requests, store the requests in a durable datastore, and process them at the speed which your system can handle.


Serverless computing allows you to scale much quicker than with server-based applications, but that means application architects should always consider the effects of scaling to your downstream services. Always keep in mind cost, speed, and reliability when you’re building your serverless applications.

Our next post in this series will discuss the different ways to invoke your Lambda functions and how to design your applications appropriately.

About the Author

George MaoGeorge Mao is a Specialist Solutions Architect at Amazon Web Services, focused on the Serverless platform. George is responsible for helping customers design and operate Serverless applications using services like Lambda, API Gateway, Cognito, and DynamoDB. He is a regular speaker at AWS Summits, re:Invent, and various tech events. George is a software engineer and enjoys contributing to open source projects, delivering technical presentations at technology events, and working with customers to design their applications in the Cloud. George holds a Bachelor of Computer Science and Masters of IT from Virginia Tech.

[$] CVE-less vulnerabilities

Post Syndicated from jake original

More bugs in free software are being found these days, which is good for
many reasons, but there are some possible downsides to that as well. In
addition, projects like OSS-Fuzz are
finding lots of bugs in an automated fashion—many of which may be security
relevant. The sheer number of bugs being reported is overwhelming many
(most?) free-software projects, which simply do not have enough eyeballs to
fix, or even triage, many of the reports they receive. A discussion about
that is currently playing out on the oss-security mailing list.

UFC: Online Platforms Should Proactively Prevent Streaming Piracy

Post Syndicated from Ernesto original

With millions of dedicated fans around the world, Mixed Martial Arts (MMA) events are extremely popular.

They are also relatively expensive and as a result, unauthorized broadcasts are thriving.

For most popular fight cards, dozens of dedicated pirate streams are queued up via unauthorized IPTV services, streaming torrents, and streaming sites, in the latter case often masked with an overlay of ads. At the same time, unauthorized rebroadcasts also appear on more traditional Internet platforms, such as YouTube, Facebook, and Twitter.

This is a thorn in the size of rightsholders, including the UFC, which dominates the MMA fighting scene. To tackle the problem the UFC has employed various anti-piracy strategies. Most recently, it contracted Stream Enforcement, a company that specializes in taking down pirated broadcasts.

In addition, the MMA promoter also involves itself in the lawmaking process. Just a few weeks ago, UFC General Counsel Riché McKnight, shared his anti-piracy vision with the Senate Committee on the Judiciary.

One of the main goals for the UFC is to criminalize unauthorized streaming. Unlike downloading, streaming is currently categorized as a public performance instead of distribution, which is punishable as a misdemeanor, instead of a felony.

The Senators made note of this call, which was shared by another major sports outfit, the NBA. They also had some additional questions, however, which McKnight could answer on paper later, so it could be added to the record.

These answers, which were just published, show that the UFC is not satisfied with how some social media companies and other online services address the pirate streaming issue.

McKnight explains that the UFC has takedown tool arrangements with several social media companies, but adds that online platforms have neglected its requests to combat illegal streaming more effectively.

“We believe communication, coordination, and cooperation could be greatly improved. Our general experience is that those subject to the Digital Millennium Copyright Act (DMCA) use it as a floor and do the minimum required to be in compliance,” McKnight notes.

The UFC notes that Facebook recently bettered its communication and ‘slightly’ improved its takedown response but overall, more could be done. However, most online services appear to be reluctant to voluntarily do more than the law requires, which means that in order to trigger change, the law should change.

“Private, voluntary partnerships [with online platforms] are not sufficient to combat online piracy. Addressing this problem requires a new approach that includes a strong legal framework, a combination of private and public enforcement, and enhanced cooperation with our international partners,” McKnight writes.

Criminalizing streaming is a step forward, according to the UFC. However, that doesn’t affect the platforms that host these streams, as these are protected by the DMCA’s safe harbor provisions.

According to the UFC’s General Counsel, Congress should consider other options as well. In particular, changes to the legal framework that will motivate social media companies and other online platforms to proactively prevent piracy.

“Congress should examine how best to properly incentivize platform providers to protect copyrighted online streaming content,” McKnight writes.

“Transitioning from a reactive ‘take down’ regime to a proactive ‘prevention’ regime would better protect and enhance a vibrant online ecosystem,” he adds.

McKnight specifically mentions policies to effectively ban repeat infringers, which is already part of the DMCA, but not always properly implemented.

While not specifically mentioned, the words “proactive” and “prevention” are reminiscent of the EU’s Article 17, which could potentially lead to upload filters.  The UFC doesn’t reference filters here, but other rightsholders have in the past.

Later this year, the US Copyright Office is expected to issue a report on the effectiveness of the DMCA’s safe harbor provisions. This will be based on input from a variety of stakeholders, some of which discussed filtering requirements.

The UFC hopes that the Copyright Office report will further help Congress to shape a more effective legal framework to tackle online streaming.

A copy of the written responses to the questions from the Senate Committee on the Judiciary is available here (pdf).

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN reviews, discounts, offers and coupons.

GitLab 12.0

Post Syndicated from ris original

GitLab 12.0 has been released. “GitLab
gives users the ability to automatically create review apps for each merge request. This allows anyone to see how the design or UX has been changed.

In GitLab 12.0, we are expanding the ability to discuss those changes by
bringing the ability to insert visual review
directly into the Review App itself. With a small code snippet,
users can enable designers, product managers, and other stakeholders to
quickly provide feedback on a merge request without leaving the
app.” Other features include the ability to easily access a
project’s Dependency List, restrict access by IP address, and much more.

Performance updates to Apache Spark in Amazon EMR 5.24 – Up to 13x better performance compared to Amazon EMR 5.16

Post Syndicated from Paul Codding original

Amazon EMR release 5.24.0 includes several optimizations in Spark that improve query performance. To evaluate the performance improvements, we used TPC-DS benchmark queries with 3-TB scale and ran them on a 6-node c4.8xlarge EMR cluster with data in Amazon S3. We observed up to 13X better query performance on EMR 5.24 compared to EMR 5.16 when operating with a similar configuration.

Customers use Spark for a wide array of analytics use cases ranging from large-scale transformations to streaming, data science, and machine learning. They choose to run Spark on EMR because EMR provides the latest, stable open source community innovations, performant storage with Amazon S3, and the unique cost savings capabilities of Spot Instances and Auto Scaling.

Each monthly EMR release offers the latest open source packages, alongside new features such as multiple master nodes, and cluster reconfiguration. The team also adds performance improvements with each release.

Each of those optimizations helps you run faster and reduce costs. With EMR 5.24, we have made several new optimizations and are detailing three critical ones in this post.


To get started with EMR, sign into the console, launch a cluster, and process data.

To replicate the setup for the benchmarking queries, use the following configuration:

  • Applications installed on the cluster: Ganglia, Hive, Spark, Hadoop (installed by default).
  • EMR release: EMR 5.24.0
  • Cluster configuration
    • Master instance group: 1 c4.8xlarge instance with 512 GiB of GP2 EBS storage (4 volumes of 128 GiB each)
    • Core instance group: 5 c4.8xlarge instances with 512 GiB of GP2 EBS storage (4 volumes of 128 GiB each)
yarn-siteyarn.nodemanager.resource.memory-mb : 53248
yarn.scheduler.maximum-allocation-vcores : 36
spark-defaultsspark.executor.memory : 4743m
spark.driver.memory : 2g
spark.sql.optimizer.distinctBeforeIntersect.enabled : true
spark.sql.dynamicPartitionPruning.enabled : true
spark.sql.optimizer.flattenScalarSubqueriesWithAggregates.enabled : true
spark.executor.cores : 4
spark.executor.memoryOverhead : 890m

Results observed using TPC-DS benchmarks

The following two graphs compare the total aggregate runtime and geometric mean for all queries in the TPC-DS 3TB query dataset between the EMR releases.

The per-query runtime improvement between EMR 5.16 and EMR 5.24 is also illustrated in the following chart. The horizontal axis shows each of the queries in the TPC-DS 3 TB benchmark. The vertical axis shows the orders of magnitude of performance improvement seen in EMR 5.24.0 relative to EMR 5.16.0 as measured by query execution time. The largest performance improvements can be seen in 26 of the queries. In each of these queries, the performance was at least 2X better than EMR 5.16.

Performance optimizations in EMR 5.24

While AWS made several incremental performance improvements aggregating to the overall speedup, this post describes three major improvements in EMR 5.24 that affect the most common customer workloads:

  • Dynamic partition pruning
  • Flatten scalar subqueries

Dynamic partition pruning

Dynamic partition pruning improves job performance by selecting specific partitions within a table that must be read and processed for a query. By reducing the amount of data read and processed, queries run faster. The open source version of Spark (2.4.2) only supports pushing down static predicates that can be resolved at plan time. Examples of static predicate push down include the following:

partition_col = 5

partition_col IN (1,3,5)

partition_col BETWEEN 1 AND 3

partition_col = 1 + 3

With dynamic partition pruning turned on, Spark on EMR infers the partitions that must be read at runtime. Dynamic partition pruning is disabled by default, and can be enabled by setting the Spark property spark.sql.dynamicPartitionPruning.enabled from within Spark or when creating clusters. For more information, see Configure Spark.

Here’s an example that joins two tables and relies on dynamic partition pruning to improve performance. The store_sales table contains total sales data partitioned by region, and store_regions table contains a mapping of regions for each country. In this representative query, you want to only get data from a specific country.

SELECT ss.quarter, ss.region,, ss.total_sales 
FROM store_sales ss, store_regions sr
WHERE ss.region sr.region AND = ’North America’

Without dynamic partition pruning, this query reads all regions, before filtering out the subset of regions that match the results of the subquery. With dynamic partition pruning, only the partitions for the regions returned in the subquery are read and processed. This saves time and resources by both reading less data from storage, and processing fewer records.

The following graph shows the performance improvements to Queries 72, 80, 17, and 25 from the TPC-DS suite that we tested with 3-TB data.

Flatten scalar subqueries

This optimization can improve query performance where multiple conditions must be applied to rows from a specific table. The optimization prevents the table from being read multiple times for each condition. This optimization detects such cases, and optimizes the query to read the table only one time.

Flatten scalar subqueries is disabled by default and can be enabled by setting the Spark property spark.sql.optimizer.flattenScalarSubqueriesWithAggregates.enabled from within Spark or when creating clusters.

To give an example of how this works, use the same total_sales table from the previous optimization. In this example, you want to group stores by their average sales when their average sales are in between specific ranges.

SELECT (SELECT avg(total_sales) FROM store_sales 
WHERE total_sales BETWEEN 5000000 AND 10000000) AS group1, 
(SELECT avg(total_sales) FROM store_sales 
WHERE total_sales BETWEEN 10000000 AND 15000000) AS group2, 
(SELECT avg(total_sales) FROM store_sales 
WHERE total_sales BETWEEN 15000000 AND 20000000) AS group3  

With this optimization disabled, the total_sales table is read for each sub query. With the optimization enabled, the query is rewritten as follows to apply each of the conditions to the rows returned by reading the table only one time.

SELECT c1 AS group1, c2 AS group2, c3 AS group3 
FROM (SELECT avg (IF(total_sales BETWEEN 5000000 AND 10000000, total_sales, null)) AS c1, 
avg (IF(total_sales BETWEEN 10000000 AND 15000000, total_sales, null)) AS c2, 
avg (IF(total_sales BETWEEN 15000000 AND 20000000, total_sales, null)) AS c3 FROM store_sales);  

This optimization saves time and resources by both reading less data from storage, and processing fewer records.

To illustrate, take the example of Q9 from the TPCDS suite. The query runs 2.9x faster in version 5.24 compared to 5.16, when the relevant Spark property is switched on.


When producing the intersection of two collections, the result of that intersection is a set of unique values found in each collection. When dealing with large collections, many duplicate records must be both processed, and shuffled between hosts to finally calculate the intersection. This optimization eliminates duplicate values in each collection before computing the intersection, improving performance by reducing the amount of data shuffled between hosts.

This optimization is disabled by default and can be enabled by setting the Spark property spark.sql.optimizer.distinctBeforeIntersect.enabled from within Spark or when creating clusters.

For example (simplified from TPC-DS query14), you want to find all of the brands that are sold both in store and catalog sale channels. In this example, the store_sales table contains sale made through the store channel, the catalog_sales table contains sale made through catalog, and the item table contains each unique product’s formulation (e.g. brand, manufactuer).

(SELECT item.brand ss_brand FROM store_sales, item
WHERE store_sales.item_id = item.item_id)
(SELECT item.brand cs_brand FROM catalog_sales, item 
WHERE catalog_sales.item_id = item.item_id) 

With this optimization disabled, the first SELECT statement produces 2,600,000 records (same number of records as store_sales) with only 1,200 unique brands. The second SELECT statement produces 1,500,000 records (same number of records as catalog_sales) with 300 unique brands. This results in all 4,100,000 rows being fed into the intersect operation to produce the 200 brands that exist in both results.

With the optimization enabled, a distinct operation is performed on each collection before being fed into the intersect operator, resulting in only 1,200 + 300 records being fed into the intersect operator. This optimization saves time and resources by shuffling less data between hosts.


With each of these performance optimizations to Apache Spark, you benefit from better query performance on EMR 5.24 compared to EMR 5.16. We look forward to feedback on how these optimizations benefit your real world workloads.

Stay tuned as we roll out additional updates to improve Apache Spark performance in EMR. To keep up-to-date, subscribe to the Big Data blog’s RSS feed to learn about more great Apache Spark optimizations, configuration best practices, and tuning advice. Be sure not to miss other great optimizations like using S3 Select with Spark, and the EMRFS S3-Optimized Committer from previous EMR releases.


About the Author

Paul Codding is a senior product manager for EMR at Amazon Web Services.





Peter Gvozdjak is a senior engineering manager for EMR at Amazon Web Services.





Joseph Marques is a principal engineer for EMR at Amazon Web Services.





Yuzhou Sun is a software development engineer for EMR at Amazon Web Services.





Atul Payapilly is a software development engineer for EMR at Amazon Web Services.





Surya Vadan Akivikolanu is a software development engineer for EMR at Amazon Web Services.





Deeper Connection with the Local Tech Community in India

Post Syndicated from Tingting (Teresa) Huang original

Deeper Connection with the Local Tech Community in India

On June 6th 2019, Cloudflare hosted the first ever customer event in a beautiful and green district of Bangalore, India. More than 60 people, including executives, developers, engineers, and even university students, have attended the half day forum.

Deeper Connection with the Local Tech Community in India

The forum kicked off with a series of presentations on the current DDoS landscape, the cyber security trends, the Serverless computing and Cloudflare’s Workers. Trey Quinn, Cloudflare Global Head of Solution Engineering, gave a brief introduction on the evolution of edge computing.

Deeper Connection with the Local Tech Community in India

We also invited business and thought leaders across various industries to share their insights and best practices on cyber security and performance strategy. Some of the keynote and penal sessions included live demos from our customers.

Deeper Connection with the Local Tech Community in India

At this event, the guests had gained first-hand knowledge on the latest technology. They also learned some insider tactics that will help them to protect their business, to accelerate the performance and to identify the quick-wins in a complex internet environment.

Deeper Connection with the Local Tech Community in India

To conclude the event, we arrange some dinner for the guests to network and to enjoy a cool summer night.

Deeper Connection with the Local Tech Community in India

Through this event, Cloudflare has strengthened the connection with the local tech community. The success of the event cannot be separated from the constant improvement from Cloudflare and the continuous support from our customers in India.

As the old saying goes, भारत महान है (India is great). India is such an important market in the region. Cloudflare will enhance the investment and engagement in providing better services and user experience for India customers.

The collective thoughts of the interwebz

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