Tag Archives: c3

Security updates for Monday

Post Syndicated from ris original https://lwn.net/Articles/756489/rss

Security updates have been issued by CentOS (procps, xmlrpc, and xmlrpc3), Debian (batik, prosody, redmine, wireshark, and zookeeper), Fedora (jasper, kernel, poppler, and xmlrpc), Mageia (git and wireshark), Red Hat (rh-java-common-xmlrpc), Slackware (git), SUSE (bzr, dpdk-thunderxdpdk, and ocaml), and Ubuntu (exempi).

Security updates for Friday

Post Syndicated from ris original https://lwn.net/Articles/756260/rss

Security updates have been issued by Debian (kernel, procps, and tiff), Fedora (ca-certificates, chromium, and git), Mageia (kernel, kernel-linus, kernel-tmb, and libvirt), openSUSE (chromium and xen), Oracle (procps, xmlrpc, and xmlrpc3), Red Hat (xmlrpc and xmlrpc3), Scientific Linux (procps, xmlrpc, and xmlrpc3), SUSE (HA kernel modules and kernel), and Ubuntu (libytnef and python-oslo.middleware).

From Framework to Function: Deploying AWS Lambda Functions for Java 8 using Apache Maven Archetype

Post Syndicated from Ryosuke Iwanaga original https://aws.amazon.com/blogs/compute/from-framework-to-function-deploying-aws-lambda-functions-for-java-8-using-apache-maven-archetype/

As a serverless computing platform that supports Java 8 runtime, AWS Lambda makes it easy to run any type of Java function simply by uploading a JAR file. To help define not only a Lambda serverless application but also Amazon API Gateway, Amazon DynamoDB, and other related services, the AWS Serverless Application Model (SAM) allows developers to use a simple AWS CloudFormation template.

AWS provides the AWS Toolkit for Eclipse that supports both Lambda and SAM. AWS also gives customers an easy way to create Lambda functions and SAM applications in Java using the AWS Command Line Interface (AWS CLI). After you build a JAR file, all you have to do is type the following commands:

aws cloudformation package 
aws cloudformation deploy

To consolidate these steps, customers can use Archetype by Apache Maven. Archetype uses a predefined package template that makes getting started to develop a function exceptionally simple.

In this post, I introduce a Maven archetype that allows you to create a skeleton of AWS SAM for a Java function. Using this archetype, you can generate a sample Java code example and an accompanying SAM template to deploy it on AWS Lambda by a single Maven action.

Prerequisites

Make sure that the following software is installed on your workstation:

  • Java
  • Maven
  • AWS CLI
  • (Optional) AWS SAM CLI

Install Archetype

After you’ve set up those packages, install Archetype with the following commands:

git clone https://github.com/awslabs/aws-serverless-java-archetype
cd aws-serverless-java-archetype
mvn install

These are one-time operations, so you don’t run them for every new package. If you’d like, you can add Archetype to your company’s Maven repository so that other developers can use it later.

With those packages installed, you’re ready to develop your new Lambda Function.

Start a project

Now that you have the archetype, customize it and run the code:

cd /path/to/project_home
mvn archetype:generate \
  -DarchetypeGroupId=com.amazonaws.serverless.archetypes \
  -DarchetypeArtifactId=aws-serverless-java-archetype \
  -DarchetypeVersion=1.0.0 \
  -DarchetypeRepository=local \ # Forcing to use local maven repository
  -DinteractiveMode=false \ # For batch mode
  # You can also specify properties below interactively if you omit the line for batch mode
  -DgroupId=YOUR_GROUP_ID \
  -DartifactId=YOUR_ARTIFACT_ID \
  -Dversion=YOUR_VERSION \
  -DclassName=YOUR_CLASSNAME

You should have a directory called YOUR_ARTIFACT_ID that contains the files and folders shown below:

├── event.json
├── pom.xml
├── src
│   └── main
│       ├── java
│       │   └── Package
│       │       └── Example.java
│       └── resources
│           └── log4j2.xml
└── template.yaml

The sample code is a working example. If you install SAM CLI, you can invoke it just by the command below:

cd YOUR_ARTIFACT_ID
mvn -P invoke verify
[INFO] Scanning for projects...
[INFO]
[INFO] ---------------------------< com.riywo:foo >----------------------------
[INFO] Building foo 1.0
[INFO] --------------------------------[ jar ]---------------------------------
...
[INFO] --- maven-jar-plugin:3.0.2:jar (default-jar) @ foo ---
[INFO] Building jar: /private/tmp/foo/target/foo-1.0.jar
[INFO]
[INFO] --- maven-shade-plugin:3.1.0:shade (shade) @ foo ---
[INFO] Including com.amazonaws:aws-lambda-java-core:jar:1.2.0 in the shaded jar.
[INFO] Replacing /private/tmp/foo/target/lambda.jar with /private/tmp/foo/target/foo-1.0-shaded.jar
[INFO]
[INFO] --- exec-maven-plugin:1.6.0:exec (sam-local-invoke) @ foo ---
2018/04/06 16:34:35 Successfully parsed template.yaml
2018/04/06 16:34:35 Connected to Docker 1.37
2018/04/06 16:34:35 Fetching lambci/lambda:java8 image for java8 runtime...
java8: Pulling from lambci/lambda
Digest: sha256:14df0a5914d000e15753d739612a506ddb8fa89eaa28dcceff5497d9df2cf7aa
Status: Image is up to date for lambci/lambda:java8
2018/04/06 16:34:37 Invoking Package.Example::handleRequest (java8)
2018/04/06 16:34:37 Decompressing /tmp/foo/target/lambda.jar
2018/04/06 16:34:37 Mounting /private/var/folders/x5/ldp7c38545v9x5dg_zmkr5kxmpdprx/T/aws-sam-local-1523000077594231063 as /var/task:ro inside runtime container
START RequestId: a6ae19fe-b1b0-41e2-80bc-68a40d094d74 Version: $LATEST
Log output: Greeting is 'Hello Tim Wagner.'
END RequestId: a6ae19fe-b1b0-41e2-80bc-68a40d094d74
REPORT RequestId: a6ae19fe-b1b0-41e2-80bc-68a40d094d74	Duration: 96.60 ms	Billed Duration: 100 ms	Memory Size: 128 MB	Max Memory Used: 7 MB

{"greetings":"Hello Tim Wagner."}


[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 10.452 s
[INFO] Finished at: 2018-04-06T16:34:40+09:00
[INFO] ------------------------------------------------------------------------

This maven goal invokes sam local invoke -e event.json, so you can see the sample output to greet Tim Wagner.

To deploy this application to AWS, you need an Amazon S3 bucket to upload your package. You can use the following command to create a bucket if you want:

aws s3 mb s3://YOUR_BUCKET --region YOUR_REGION

Now, you can deploy your application by just one command!

mvn deploy \
    -DawsRegion=YOUR_REGION \
    -Ds3Bucket=YOUR_BUCKET \
    -DstackName=YOUR_STACK
[INFO] Scanning for projects...
[INFO]
[INFO] ---------------------------< com.riywo:foo >----------------------------
[INFO] Building foo 1.0
[INFO] --------------------------------[ jar ]---------------------------------
...
[INFO] --- exec-maven-plugin:1.6.0:exec (sam-package) @ foo ---
Uploading to aws-serverless-java/com.riywo:foo:1.0/924732f1f8e4705c87e26ef77b080b47  11657 / 11657.0  (100.00%)
Successfully packaged artifacts and wrote output template to file target/sam.yaml.
Execute the following command to deploy the packaged template
aws cloudformation deploy --template-file /private/tmp/foo/target/sam.yaml --stack-name <YOUR STACK NAME>
[INFO]
[INFO] --- maven-deploy-plugin:2.8.2:deploy (default-deploy) @ foo ---
[INFO] Skipping artifact deployment
[INFO]
[INFO] --- exec-maven-plugin:1.6.0:exec (sam-deploy) @ foo ---

Waiting for changeset to be created..
Waiting for stack create/update to complete
Successfully created/updated stack - archetype
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 37.176 s
[INFO] Finished at: 2018-04-06T16:41:02+09:00
[INFO] ------------------------------------------------------------------------

Maven automatically creates a shaded JAR file, uploads it to your S3 bucket, replaces template.yaml, and creates and updates the CloudFormation stack.

To customize the process, modify the pom.xml file. For example, to avoid typing values for awsRegion, s3Bucket or stackName, write them inside pom.xml and check in your VCS. Afterward, you and the rest of your team can deploy the function by typing just the following command:

mvn deploy

Options

Lambda Java 8 runtime has some types of handlers: POJO, Simple type and Stream. The default option of this archetype is POJO style, which requires to create request and response classes, but they are baked by the archetype by default. If you want to use other type of handlers, you can use handlerType property like below:

## POJO type (default)
mvn archetype:generate \
 ...
 -DhandlerType=pojo

## Simple type - String
mvn archetype:generate \
 ...
 -DhandlerType=simple

### Stream type
mvn archetype:generate \
 ...
 -DhandlerType=stream

See documentation for more details about handlers.

Also, Lambda Java 8 runtime supports two types of Logging class: Log4j 2 and LambdaLogger. This archetype creates LambdaLogger implementation by default, but you can use Log4j 2 if you want:

## LambdaLogger (default)
mvn archetype:generate \
 ...
 -Dlogger=lambda

## Log4j 2
mvn archetype:generate \
 ...
 -Dlogger=log4j2

If you use LambdaLogger, you can delete ./src/main/resources/log4j2.xml. See documentation for more details.

Conclusion

So, what’s next? Develop your Lambda function locally and type the following command: mvn deploy !

With this Archetype code example, available on GitHub repo, you should be able to deploy Lambda functions for Java 8 in a snap. If you have any questions or comments, please submit them below or leave them on GitHub.

Engineering deep dive: Encoding of SCTs in certificates

Post Syndicated from Let's Encrypt - Free SSL/TLS Certificates original https://letsencrypt.org/2018/04/04/sct-encoding.html

<p>Let&rsquo;s Encrypt recently <a href="https://community.letsencrypt.org/t/signed-certificate-timestamps-embedded-in-certificates/57187">launched SCT embedding in
certificates</a>.
This feature allows browsers to check that a certificate was submitted to a
<a href="https://en.wikipedia.org/wiki/Certificate_Transparency">Certificate Transparency</a>
log. As part of the launch, we did a thorough review
that the encoding of Signed Certificate Timestamps (SCTs) in our certificates
matches the relevant specifications. In this post, I&rsquo;ll dive into the details.
You&rsquo;ll learn more about X.509, ASN.1, DER, and TLS encoding, with references to
the relevant RFCs.</p>

<p>Certificate Transparency offers three ways to deliver SCTs to a browser: In a
TLS extension, in stapled OCSP, or embedded in a certificate. We chose to
implement the embedding method because it would just work for Let&rsquo;s Encrypt
subscribers without additional work. In the SCT embedding method, we submit
a &ldquo;precertificate&rdquo; with a <a href="#poison">poison extension</a> to a set of
CT logs, and get back SCTs. We then issue a real certificate based on the
precertificate, with two changes: The poison extension is removed, and the SCTs
obtained earlier are added in another extension.</p>

<p>Given a certificate, let&rsquo;s first look for the SCT list extension. According to CT (<a href="https://tools.ietf.org/html/rfc6962#section-3.3">RFC 6962
section 3.3</a>),
the extension OID for a list of SCTs is <code>1.3.6.1.4.1.11129.2.4.2</code>. An <a href="http://www.hl7.org/Oid/information.cfm">OID (object
ID)</a> is a series of integers, hierarchically
assigned and globally unique. They are used extensively in X.509, for instance
to uniquely identify extensions.</p>

<p>We can <a href="https://acme-v01.api.letsencrypt.org/acme/cert/031f2484307c9bc511b3123cb236a480d451">download an example certificate</a>,
and view it using OpenSSL (if your OpenSSL is old, it may not display the
detailed information):</p>

<pre><code>$ openssl x509 -noout -text -inform der -in Downloads/031f2484307c9bc511b3123cb236a480d451

CT Precertificate SCTs:
Signed Certificate Timestamp:
Version : v1(0)
Log ID : DB:74:AF:EE:CB:29:EC:B1:FE:CA:3E:71:6D:2C:E5:B9:
AA:BB:36:F7:84:71:83:C7:5D:9D:4F:37:B6:1F:BF:64
Timestamp : Mar 29 18:45:07.993 2018 GMT
Extensions: none
Signature : ecdsa-with-SHA256
30:44:02:20:7E:1F:CD:1E:9A:2B:D2:A5:0A:0C:81:E7:
13:03:3A:07:62:34:0D:A8:F9:1E:F2:7A:48:B3:81:76:
40:15:9C:D3:02:20:65:9F:E9:F1:D8:80:E2:E8:F6:B3:
25:BE:9F:18:95:6D:17:C6:CA:8A:6F:2B:12:CB:0F:55:
FB:70:F7:59:A4:19
Signed Certificate Timestamp:
Version : v1(0)
Log ID : 29:3C:51:96:54:C8:39:65:BA:AA:50:FC:58:07:D4:B7:
6F:BF:58:7A:29:72:DC:A4:C3:0C:F4:E5:45:47:F4:78
Timestamp : Mar 29 18:45:08.010 2018 GMT
Extensions: none
Signature : ecdsa-with-SHA256
30:46:02:21:00:AB:72:F1:E4:D6:22:3E:F8:7F:C6:84:
91:C2:08:D2:9D:4D:57:EB:F4:75:88:BB:75:44:D3:2F:
95:37:E2:CE:C1:02:21:00:8A:FF:C4:0C:C6:C4:E3:B2:
45:78:DA:DE:4F:81:5E:CB:CE:2D:57:A5:79:34:21:19:
A1:E6:5B:C7:E5:E6:9C:E2
</code></pre>

<p>Now let&rsquo;s go a little deeper. How is that extension represented in
the certificate? Certificates are expressed in
<a href="https://en.wikipedia.org/wiki/Abstract_Syntax_Notation_One">ASN.1</a>,
which generally refers to both a language for expressing data structures
and a set of formats for encoding them. The most common format,
<a href="https://en.wikipedia.org/wiki/X.690#DER_encoding">DER</a>,
is a tag-length-value format. That is, to encode an object, first you write
down a tag representing its type (usually one byte), then you write
down a number expressing how long the object is, then you write down
the object contents. This is recursive: An object can contain multiple
objects within it, each of which has its own tag, length, and value.</p>

<p>One of the cool things about DER and other tag-length-value formats is that you
can decode them to some degree without knowing what they mean. For instance, I
can tell you that 0x30 means the data type &ldquo;SEQUENCE&rdquo; (a struct, in ASN.1
terms), and 0x02 means &ldquo;INTEGER&rdquo;, then give you this hex byte sequence to
decode:</p>

<pre><code>30 06 02 01 03 02 01 0A
</code></pre>

<p>You could tell me right away that decodes to:</p>

<pre><code>SEQUENCE
INTEGER 3
INTEGER 10
</code></pre>

<p>Try it yourself with this great <a href="https://lapo.it/asn1js/#300602010302010A">JavaScript ASN.1
decoder</a>. However, you wouldn&rsquo;t know
what those integers represent without the corresponding ASN.1 schema (or
&ldquo;module&rdquo;). For instance, if you knew that this was a piece of DogData, and the
schema was:</p>

<pre><code>DogData ::= SEQUENCE {
legs INTEGER,
cutenessLevel INTEGER
}
</code></pre>

<p>You&rsquo;d know this referred to a three-legged dog with a cuteness level of 10.</p>

<p>We can take some of this knowledge and apply it to our certificates. As a first
step, convert the above certificate to hex with
<code>xxd -ps &lt; Downloads/031f2484307c9bc511b3123cb236a480d451</code>. You can then copy
and paste the result into
<a href="https://lapo.it/asn1js">lapo.it/asn1js</a> (or use <a href="https://lapo.it/asn1js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this handy link</a>). You can also run <code>openssl asn1parse -i -inform der -in Downloads/031f2484307c9bc511b3123cb236a480d451</code> to use OpenSSL&rsquo;s parser, which is less easy to use in some ways, but easier to copy and paste.</p>

<p>In the decoded data, we can find the OID <code>1.3.6.1.4.1.11129.2.4.2</code>, indicating
the SCT list extension. Per <a href="https://tools.ietf.org/html/rfc5280#page-17">RFC 5280, section
4.1</a>, an extension is defined:</p>

<pre><code>Extension ::= SEQUENCE {
extnID OBJECT IDENTIFIER,
critical BOOLEAN DEFAULT FALSE,
extnValue OCTET STRING
— contains the DER encoding of an ASN.1 value
— corresponding to the extension type identified
— by extnID
}
</code></pre>

<p>We&rsquo;ve found the <code>extnID</code>. The &ldquo;critical&rdquo; field is omitted because it has the
default value (false). Next up is the <code>extnValue</code>. This has the type
<code>OCTET STRING</code>, which has the tag &ldquo;0x04&rdquo;. <code>OCTET STRING</code> means &ldquo;here&rsquo;s
a bunch of bytes!&rdquo; In this case, as described by the spec, those bytes
happen to contain more DER. This is a fairly common pattern in X.509
to deal with parameterized data. For instance, this allows defining a
structure for extensions without knowing ahead of time all the structures
that a future extension might want to carry in its value. If you&rsquo;re a C
programmer, think of it as a <code>void*</code> for data structures. If you prefer Go,
think of it as an <code>interface{}</code>.</p>

<p>Here&rsquo;s that <code>extnValue</code>:</p>

<pre><code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
</code></pre>

<p>That&rsquo;s tag &ldquo;0x04&rdquo;, meaning <code>OCTET STRING</code>, followed by &ldquo;0x81 0xF5&rdquo;, meaning
&ldquo;this string is 245 bytes long&rdquo; (the 0x81 prefix is part of <a href="#variable-length">variable length
number encoding</a>).</p>

<p>According to <a href="https://tools.ietf.org/html/rfc6962#section-3.3">RFC 6962, section
3.3</a>, &ldquo;obtained SCTs
can be directly embedded in the final certificate, by encoding the
SignedCertificateTimestampList structure as an ASN.1 <code>OCTET STRING</code>
and inserting the resulting data in the TBSCertificate as an X.509v3
certificate extension&rdquo;</p>

<p>So, we have an <code>OCTET STRING</code>, all&rsquo;s good, right? Except if you remove the
tag and length from extnValue to get its value, you&rsquo;re left with:</p>

<pre><code>04 81 F2 00F0007500DB74AFEEC…
</code></pre>

<p>There&rsquo;s that &ldquo;0x04&rdquo; tag again, but with a shorter length. Why
do we nest one <code>OCTET STRING</code> inside another? It&rsquo;s because the
contents of extnValue are required by RFC 5280 to be valid DER, but a
SignedCertificateTimestampList is not encoded using DER (more on that
in a minute). So, by RFC 6962, a SignedCertificateTimestampList is wrapped in an
<code>OCTET STRING</code>, which is wrapped in another <code>OCTET STRING</code> (the extnValue).</p>

<p>Once we decode that second <code>OCTET STRING</code>, we&rsquo;re left with the contents:</p>

<pre><code>00F0007500DB74AFEEC…
</code></pre>

<p>&ldquo;0x00&rdquo; isn&rsquo;t a valid tag in DER. What is this? It&rsquo;s TLS encoding. This is
defined in <a href="https://tools.ietf.org/html/rfc5246#section-4">RFC 5246, section 4</a>
(the TLS 1.2 RFC). TLS encoding, like ASN.1, has both a way to define data
structures and a way to encode those structures. TLS encoding differs
from DER in that there are no tags, and lengths are only encoded when necessary for
variable-length arrays. Within an encoded structure, the type of a field is determined by
its position, rather than by a tag. This means that TLS-encoded structures are
more compact than DER structures, but also that they can&rsquo;t be processed without
knowing the corresponding schema. For instance, here&rsquo;s the top-level schema from
<a href="https://tools.ietf.org/html/rfc6962#section-3.3">RFC 6962, section 3.3</a>:</p>

<pre><code> The contents of the ASN.1 OCTET STRING embedded in an OCSP extension
or X509v3 certificate extension are as follows:

opaque SerializedSCT&lt;1..2^16-1&gt;;

struct {
SerializedSCT sct_list &lt;1..2^16-1&gt;;
} SignedCertificateTimestampList;

Here, &quot;SerializedSCT&quot; is an opaque byte string that contains the
serialized TLS structure.
</code></pre>

<p>Right away, we&rsquo;ve found one of those variable-length arrays. The length of such
an array (in bytes) is always represented by a length field just big enough to
hold the max array size. The max size of an <code>sct_list</code> is 65535 bytes, so the
length field is two bytes wide. Sure enough, those first two bytes are &ldquo;0x00
0xF0&rdquo;, or 240 in decimal. In other words, this <code>sct_list</code> will have 240 bytes. We
don&rsquo;t yet know how many SCTs will be in it. That will become clear only by
continuing to parse the encoded data and seeing where each struct ends (spoiler
alert: there are two SCTs!).</p>

<p>Now we know the first SerializedSCT starts with <code>0075…</code>. SerializedSCT
is itself a variable-length field, this time containing <code>opaque</code> bytes (much like <code>OCTET STRING</code>
back in the ASN.1 world). Like SignedCertificateTimestampList, it has a max size
of 65535 bytes, so we pull off the first two bytes and discover that the first
SerializedSCT is 0x0075 (117 decimal) bytes long. Here&rsquo;s the whole thing, in
hex:</p>

<pre><code>00DB74AFEECB29ECB1FECA3E716D2CE5B9AABB36F7847183C75D9D4F37B61FBF64000001627313EB19000004030046304402207E1FCD1E9A2BD2A50A0C81E713033A0762340DA8F91EF27A48B3817640159CD30220659FE9F1D880E2E8F6B325BE9F18956D17C6CA8A6F2B12CB0F55FB70F759A419
</code></pre>

<p>This can be decoded using the TLS encoding struct defined in <a href="https://tools.ietf.org/html/rfc6962#page-13">RFC 6962, section
3.2</a>:</p>

<pre><code>enum { v1(0), (255) }
Version;

struct {
opaque key_id[32];
} LogID;

opaque CtExtensions&lt;0..2^16-1&gt;;

struct {
Version sct_version;
LogID id;
uint64 timestamp;
CtExtensions extensions;
digitally-signed struct {
Version sct_version;
SignatureType signature_type = certificate_timestamp;
uint64 timestamp;
LogEntryType entry_type;
select(entry_type) {
case x509_entry: ASN.1Cert;
case precert_entry: PreCert;
} signed_entry;
CtExtensions extensions;
};
} SignedCertificateTimestamp;
</code></pre>

<p>Breaking that down:</p>

<pre><code># Version sct_version v1(0)
00
# LogID id (aka opaque key_id[32])
DB74AFEECB29ECB1FECA3E716D2CE5B9AABB36F7847183C75D9D4F37B61FBF64
# uint64 timestamp (milliseconds since the epoch)
000001627313EB19
# CtExtensions extensions (zero-length array)
0000
# digitally-signed struct
04030046304402207E1FCD1E9A2BD2A50A0C81E713033A0762340DA8F91EF27A48B3817640159CD30220659FE9F1D880E2E8F6B325BE9F18956D17C6CA8A6F2B12CB0F55FB70F759A419
</code></pre>

<p>To understand the &ldquo;digitally-signed struct,&rdquo; we need to turn back to <a href="https://tools.ietf.org/html/rfc5246#section-4.7">RFC 5246,
section 4.7</a>. It says:</p>

<pre><code>A digitally-signed element is encoded as a struct DigitallySigned:

struct {
SignatureAndHashAlgorithm algorithm;
opaque signature&lt;0..2^16-1&gt;;
} DigitallySigned;
</code></pre>

<p>And in <a href="https://tools.ietf.org/html/rfc5246#section-7.4.1.4.1">section
7.4.1.4.1</a>:</p>

<pre><code>enum {
none(0), md5(1), sha1(2), sha224(3), sha256(4), sha384(5),
sha512(6), (255)
} HashAlgorithm;

enum { anonymous(0), rsa(1), dsa(2), ecdsa(3), (255) }
SignatureAlgorithm;

struct {
HashAlgorithm hash;
SignatureAlgorithm signature;
} SignatureAndHashAlgorithm;
</code></pre>

<p>We have &ldquo;0x0403&rdquo;, which corresponds to sha256(4) and ecdsa(3). The next two
bytes, &ldquo;0x0046&rdquo;, tell us the length of the &ldquo;opaque signature&rdquo; field, 70 bytes in
decimal. To decode the signature, we reference <a href="https://tools.ietf.org/html/rfc4492#page-20">RFC 4492 section
5.4</a>, which says:</p>

<pre><code>The digitally-signed element is encoded as an opaque vector &lt;0..2^16-1&gt;, the
contents of which are the DER encoding corresponding to the
following ASN.1 notation.

Ecdsa-Sig-Value ::= SEQUENCE {
r INTEGER,
s INTEGER
}
</code></pre>

<p>Having dived through two layers of TLS encoding, we are now back in ASN.1 land!
We
<a href="https://lapo.it/asn1js/#304402207E1FCD1E9A2BD2A50A0C81E713033A0762340DA8F91EF27A48B3817640159CD30220659FE9F1D880E2E8F6B325BE9F18956D17C6CA8A6F2B12CB0F55FB70F759A419">decode</a>
the remaining bytes into a SEQUENCE containing two INTEGERS. And we&rsquo;re done! Here&rsquo;s the whole
extension decoded:</p>

<pre><code># Extension SEQUENCE – RFC 5280
30
# length 0x0104 bytes (260 decimal)
820104
# OBJECT IDENTIFIER
06
# length 0x0A bytes (10 decimal)
0A
# value (1.3.6.1.4.1.11129.2.4.2)
2B06010401D679020402
# OCTET STRING
04
# length 0xF5 bytes (245 decimal)
81F5
# OCTET STRING (embedded) – RFC 6962
04
# length 0xF2 bytes (242 decimal)
81F2
# Beginning of TLS encoded SignedCertificateTimestampList – RFC 5246 / 6962
# length 0xF0 bytes
00F0
# opaque SerializedSCT&lt;1..2^16-1&gt;
# length 0x75 bytes
0075
# Version sct_version v1(0)
00
# LogID id (aka opaque key_id[32])
DB74AFEECB29ECB1FECA3E716D2CE5B9AABB36F7847183C75D9D4F37B61FBF64
# uint64 timestamp (milliseconds since the epoch)
000001627313EB19
# CtExtensions extensions (zero-length array)
0000
# digitally-signed struct – RFC 5426
# SignatureAndHashAlgorithm (ecdsa-sha256)
0403
# opaque signature&lt;0..2^16-1&gt;;
# length 0x0046
0046
# DER-encoded Ecdsa-Sig-Value – RFC 4492
30 # SEQUENCE
44 # length 0x44 bytes
02 # r INTEGER
20 # length 0x20 bytes
# value
7E1FCD1E9A2BD2A50A0C81E713033A0762340DA8F91EF27A48B3817640159CD3
02 # s INTEGER
20 # length 0x20 bytes
# value
659FE9F1D880E2E8F6B325BE9F18956D17C6CA8A6F2B12CB0F55FB70F759A419
# opaque SerializedSCT&lt;1..2^16-1&gt;
# length 0x77 bytes
0077
# Version sct_version v1(0)
00
# LogID id (aka opaque key_id[32])
293C519654C83965BAAA50FC5807D4B76FBF587A2972DCA4C30CF4E54547F478
# uint64 timestamp (milliseconds since the epoch)
000001627313EB2A
# CtExtensions extensions (zero-length array)
0000
# digitally-signed struct – RFC 5426
# SignatureAndHashAlgorithm (ecdsa-sha256)
0403
# opaque signature&lt;0..2^16-1&gt;;
# length 0x0048
0048
# DER-encoded Ecdsa-Sig-Value – RFC 4492
30 # SEQUENCE
46 # length 0x46 bytes
02 # r INTEGER
21 # length 0x21 bytes
# value
00AB72F1E4D6223EF87FC68491C208D29D4D57EBF47588BB7544D32F9537E2CEC1
02 # s INTEGER
21 # length 0x21 bytes
# value
008AFFC40CC6C4E3B24578DADE4F815ECBCE2D57A579342119A1E65BC7E5E69CE2
</code></pre>

<p>One surprising thing you might notice: In the first SCT, <code>r</code> and <code>s</code> are twenty
bytes long. In the second SCT, they are both twenty-one bytes long, and have a
leading zero. Integers in DER are two&rsquo;s complement, so if the leftmost bit is
set, they are interpreted as negative. Since <code>r</code> and <code>s</code> are positive, if the
leftmost bit would be a 1, an extra byte has to be added so that the leftmost
bit can be 0.</p>

<p>This is a little taste of what goes into encoding a certificate. I hope it was
informative! If you&rsquo;d like to learn more, I recommend &ldquo;<a href="http://luca.ntop.org/Teaching/Appunti/asn1.html">A Layman&rsquo;s Guide to a
Subset of ASN.1, BER, and DER</a>.&rdquo;</p>

<p><a name="poison"></a>Footnote 1: A &ldquo;poison extension&rdquo; is defined by <a href="https://tools.ietf.org/html/rfc6962#section-3.1">RFC 6962
section 3.1</a>:</p>

<pre><code>The Precertificate is constructed from the certificate to be issued by adding a special
critical poison extension (OID `1.3.6.1.4.1.11129.2.4.3`, whose
extnValue OCTET STRING contains ASN.1 NULL data (0x05 0x00))
</code></pre>

<p>In other words, it&rsquo;s an empty extension whose only purpose is to ensure that
certificate processors will not accept precertificates as valid certificates. The
specification ensures this by setting the &ldquo;critical&rdquo; bit on the extension, which
ensures that code that doesn&rsquo;t recognize the extension will reject the whole
certificate. Code that does recognize the extension specifically as poison
will also reject the certificate.</p>

<p><a name="variable-length"></a>Footnote 2: Lengths from 0-127 are represented by
a single byte (short form). To express longer lengths, more bytes are used (long form).
The high bit (0x80) on the first byte is set to distinguish long form from short
form. The remaining bits are used to express how many more bytes to read for the
length. For instance, 0x81F5 means &ldquo;this is long form because the length is
greater than 127, but there&rsquo;s still only one byte of length (0xF5) to decode.&rdquo;</p>

All-In on Unlimited Backup

Post Syndicated from Gleb Budman original https://www.backblaze.com/blog/all-in-on-unlimited-backup/

chips on computer with cloud backup

The cloud backup industry has seen its share of tumultuousness. BitCasa, Dell DataSafe, Xdrive, and a dozen others have closed up shop. Mozy, Amazon, and Microsoft offered, but later canceled, their unlimited offerings. Recently, CrashPlan for Home customers were notified that their service was being end-of-lifed. Then today we’ve heard from Carbonite customers who are frustrated by this morning’s announcement of a price increase from Carbonite.

We believe that the fundamental goal of a cloud backup is having peace-of-mind: knowing your data — all of it — is safe. For over 10 years Backblaze has been providing that peace-of-mind by offering completely unlimited cloud backup to our customers. And we continue to be committed to that. Knowing that your cloud backup vendor is not going to disappear or fundamentally change their service is an essential element in achieving that peace-of-mind.

Committed to Unlimited Backup

When Mozy discontinued their unlimited backup on Jan 31, 2011, a lot of people asked, “Does this mean Backblaze will discontinue theirs as well?” At that time I wrote the blog post Backblaze is committed to unlimited backup. That was seven years ago. Since then we’ve continued to make Backblaze cloud backup better: dramatically speeding up backups and restores, offering the unique and very popular Restore Return Refund program, enabling direct access and sharing of any file in your backup, and more. We also introduced Backblaze Groups to enable businesses and families to manage backups — all at no additional cost.

How That’s Possible

I’d like to answer the question of “How have you been able to do this when others haven’t?

First, commitment. It’s not impossible to offer unlimited cloud backup, but it’s not easy. The Backblaze team has been committed to unlimited as a core tenet.

Second, we have pursued the technical, business, and cultural steps required to make it happen. We’ve designed our own servers, written our cloud storage software, run our own operations, and been continually focused on every place we could optimize a penny out of the cost of storage. We’ve built a culture at Backblaze that cares deeply about that.

Ensuring Peace-of-Mind

Price increases and plan changes happen in our industry, but Backblaze has consistently been the low price leader, and continues to stand by the foundational element of our service — truly unlimited backup storage. Carbonite just announced a price increase from $60 to $72/year, and while that’s not an astronomical increase, it’s important to keep in mind the service that they are providing at that rate. The basic Carbonite plan provides a service that doesn’t back up videos or external hard drives by default. We think that’s dangerous. No one wants to discover that their videos weren’t backed up after their computer dies, or have to worry about the safety and durability of their data. That is why we have continued to build on our foundation of unlimited, as well as making our service faster and more accessible. All of these serve the goal of ensuring peace-of-mind for our customers.

3 Months Free For You & A Friend

As part of our commitment to unlimited, refer your friends to receive three months of Backblaze service through March 15, 2018. When you Refer-a-Friend with your personal referral link, and they subscribe, both of you will receive three months of service added to your account. See promotion details on our Refer-a-Friend page.

Want A Reminder When Your Carbonite Subscription Runs Out?

If you’re considering switching from Carbonite, we’d love to be your new backup provider. Enter your email and the date you’d like to be reminded in the form below and you’ll get a friendly reminder email from us to start a new backup plan with Backblaze. Or, you could start a free trial today.

We think you’ll be glad you switched, and you’ll have a chance to experience some of that Backblaze peace-of-mind for your data.

Please Send Me a Reminder When I Need a New Backup Provider



 

The post All-In on Unlimited Backup appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Are the BSDs dying? Some security researchers think so (CSO)

Post Syndicated from corbet original https://lwn.net/Articles/745383/rss

Here’s a
34c3 conference report in CSO
suggesting that the BSDs are losing
developers.
von Sprundel says he easily found around 115 kernel bugs across the
three BSDs, including 30 for FreeBSD, 25 for OpenBSD, and 60 for
NetBSD. Many of these bugs he called ‘low-hanging fruit.’ He promptly
reported all the bugs, but six months later, at the time of his talk, many
remained unpatched.

‘By and large, most security flaws in the Linux kernel don’t have a long
lifetime. They get found pretty fast,’ von Sprundel says. ‘On the BSD side,
that isn’t always true. I found a bunch of bugs that have been around a
very long time.’ Many of them have been present in code for a decade or
more.”

Recent EC2 Goodies – Launch Templates and Spread Placement

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/recent-ec2-goodies-launch-templates-and-spread-placement/

We launched some important new EC2 instance types and features at AWS re:Invent. I’ve already told you about the M5, H1, T2 Unlimited and Bare Metal instances, and about Spot features such as Hibernation and the New Pricing Model. Randall told you about the Amazon Time Sync Service. Today I would like to tell you about two of the features that we launched: Spread placement groups and Launch Templates. Both features are available in the EC2 Console and from the EC2 APIs, and can be used in all of the AWS Regions in the “aws” partition:

Launch Templates
You can use launch templates to store the instance, network, security, storage, and advanced parameters that you use to launch EC2 instances, and can also include any desired tags. Each template can include any desired subset of the full collection of parameters. You can, for example, define common configuration parameters such as tags or network configurations in a template, and allow the other parameters to be specified as part of the actual launch.

Templates give you the power to set up a consistent launch environment that spans instances launched in On-Demand and Spot form, as well as through EC2 Auto Scaling and as part of a Spot Fleet. You can use them to implement organization-wide standards and to enforce best practices, and you can give your IAM users the ability to launch instances via templates while withholding the ability to do so via the underlying APIs.

Templates are versioned and you can use any desired version when you launch an instance. You can create templates from scratch, base them on the previous version, or copy the parameters from a running instance.

Here’s how you create a launch template in the Console:

Here’s how to include network interfaces, storage volumes, tags, and security groups:

And here’s how to specify advanced and specialized parameters:

You don’t have to specify values for all of these parameters in your templates; enter the values that are common to multiple instances or launches and specify the rest at launch time.

When you click Create launch template, the template is created and can be used to launch On-Demand instances, create Auto Scaling Groups, and create Spot Fleets:

The Launch Instance button now gives you the option to launch from a template:

Simply choose the template and the version, and finalize all of the launch parameters:

You can also manage your templates and template versions from the Console:

To learn more about this feature, read Launching an Instance from a Launch Template.

Spread Placement Groups
Spread placement groups indicate that you do not want the instances in the group to share the same underlying hardware. Applications that rely on a small number of critical instances can launch them in a spread placement group to reduce the odds that one hardware failure will impact more than one instance. Here are a couple of things to keep in mind when you use spread placement groups:

  • Availability Zones – A single spread placement group can span multiple Availability Zones. You can have a maximum of seven running instances per Availability Zone per group.
  • Unique Hardware – Launch requests can fail if there is insufficient unique hardware available. The situation changes over time as overall usage changes and as we add additional hardware; you can retry failed requests at a later time.
  • Instance Types – You can launch a wide variety of M4, M5, C3, R3, R4, X1, X1e, D2, H1, I2, I3, HS1, F1, G2, G3, P2, and P3 instances types in spread placement groups.
  • Reserved Instances – Instances launched into a spread placement group can make use of reserved capacity. However, you cannot currently reserve capacity for a placement group and could receive an ICE (Insufficient Capacity Error) even if you have some RI’s available.
  • Applicability – You cannot use spread placement groups in conjunction with Dedicated Instances or Dedicated Hosts.

You can create and use spread placement groups from the AWS Management Console, the AWS Command Line Interface (CLI), the AWS Tools for Windows PowerShell, and the AWS SDKs. The console has a new feature that will help you to learn how to use the command line:

You can specify an existing placement group or create a new one when you launch an EC2 instance:

To learn more, read about Placement Groups.

Jeff;

Modding Legends Team-Xecuter Announce “Future-Proof” Nintendo Switch Hack

Post Syndicated from Andy original https://torrentfreak.com/modding-legends-team-xecuter-announce-future-proof-nintendo-switch-hack-180104/

Since the advent of the first truly mass-market videogames consoles, people have dreamed about removing the protection mechanisms that prevent users from tinkering with their machines.

These modifications – which are software, hardware, or combination of the two – facilitate the running of third-party or “homebrew” code. On this front, a notable mention must go to XBMC (now known as Kodi) which ran on the original Xbox after its copy protection mechanisms had been removed.

However, these same modifications regularly open the door to mass-market piracy too, with mod-chips (hardware devices) or soft-mods (software solutions) opening up machines so that consumers can run games obtained from the Internet or elsewhere.

For the Nintendo Switch, that prospect edged closer at the end of December when Wololo reported that hackers Plutoo, Derrek, and Naehrwert had given a long presentation (video) at the 34C3 hacking conference in Germany, revealing their kernel hack for the Nintendo Switch.

While this in itself is an exciting development, fresh news from a veteran hacking group suggests that Nintendo could be in big trouble on the piracy front in the not-too-distant future.

“In the light of a recent presentation at the Chaos Communication Congress in Germany we’ve decided to come out of the woodwork and tease you all a bit with our latest upcoming product,” the legendary Team-Xecutor just announced.

While the hack announced in December requires Switch firmware 3.0 (and a copy of Pokken Tournament DX), Team-Xecutor say that their product will be universal, something which tends to suggest a fundamental flaw in the Switch system.

“This solution will work on ANY Nintendo Switch console regardless of the currently installed firmware, and will be completely future proof,” the team explain.

Xecutor say that their solution opens up the possibility of custom firmware (CFW) on Nintendo’s console. In layman’s terms, this means that those with the technical ability will be able to dictate, at least to a point, how the console functions.

“We want to move the community forward and provide a persistent, stable and fast method of running your own code and custom firmware patches on Nintendo’s latest flagship product. And we think we’ve succeeded!” the team add.

The console-modding community thrives on rumors, with various parties claiming to have made progress here and there, on this console and that, so it’s natural for people to greet this kind of announcement with a degree of skepticism. That being said, Team-Xecutor is no regular group.

With a long history of console-based meddling, Team-Xecutor’s efforts include hardware solutions for the original Playstation and Playstation 2, an array of hacks for the original Xbox (Enigmah and various Xecuter-branded solutions), plus close involvement in prominent Xbox360 mods. Their pedigree is definitely not up for debate.

For now, the team isn’t releasing any more details on the nature of the hack but they have revealed when the public can expect to get their hands on it.

“Spring 2018 or there around,” they conclude.

Team-Xecutor demo

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

2017-12-27 34c3 ден 1

Post Syndicated from Vasil Kolev original https://vasil.ludost.net/blog/?p=3373

Успявам да гледам малко лекции от 34c3 (програма, streaming).

Откриването на Charlie Stross (който ми е от любимите автори) беше доста интересно, с наблюдението, че корпорациите могат да се разглеждат като начална форма на изкуствените интелекти и всякакви интересни следствия от това, струва си да се отдели малко време и да се гледа (не знам дали ще го качи в блога си).

Лекцията за геймифицираната система за социален кредит в Китай не ми каза нещо ново и не беше особено добре представена, но е добре човек да почете за ситуацията.

Харалд Велте разказа за internet-а и BBS-ите от едно време (само че в Германия), като цяло все неща, с които едно време сме си играли. Иво ме пита дали не можем да направим някаква такава лекция или да намерим история на случвалите се неща в България. Мислех си, че вече има такова нещо, ама не мога да го намеря, някой да се сеща за хубава история на ония времена?

Лекцията за Иран имаше малко полезна информация в нея, но основно не си заслужаваше. Лекцията за Саудитска Арабия също нямаше много съдържание.

Лекцията за “Low Cost Non-Invasive Biomedical Imaging” за момента ми е любима, и трябва да си вземем едно такова нещо за в лаба. Звучи като технология, с която си струва да си играем и която може много да подобри работата на всякакви лекари.

“Defeating (Not)Petya’s Cryptography” имаше полезни моменти.

Като успея да изгледам още някакви неща, ще пиша и за тях. Който иска, може директно да ходи в initLab да гледа, тъкмо ще има с кой да коментира 🙂

Update: “The Ultimate Apollo Guidance Computer Talk” се оказа страхотно, особено архитектурата на нещото, която има вид на скалъпена с тел и тиксо.

A New AWS Government, Education, and Nonprofits Blog Post: “AWS Achieves Full Empanelment for the Delivery of Cloud Services by India’s Ministry of Electronics and Information Technology”

Post Syndicated from Craig Liebendorfer original https://aws.amazon.com/blogs/security/a-new-aws-government-education-nonprofits-blog-post-aws-achieves-full-empanelment-for-the-delivery-of-cloud-services-by-indias-ministry-of-electronics-and-information-technology/

AWS Security by Design image

AWS recently announced that Amazon Internet Services Private Limited (AISPL), an Indian subsidiary of the Amazon Group that undertakes the resale and marketing of AWS Cloud services in India, has achieved full Cloud Service Provider (CSP) empanelment and successfully completed the Standardization Testing and Quality Certification (STQC) audit from the Indian Ministry of Electronics and Information Technology (MeitY) for cloud services delivered from the AWS Asia Pacific (Mumbai) Region.

With this certification, AISPL joins a list of approved providers that meet predefined government standards of quality, availability, and security. With 52 global security certifications, attestations, assurance programs, and quality audits already achieved, such as ISO 9001, SOC1, SOC2, and SOC3, AWS is the first global cloud service provider to earn this status in India.

To read the full blog post, see AWS Achieves Full Empanelment for the Delivery of Cloud Services by India’s Ministry of Electronics and Information Technology.

– Craig

Kernel prepatch 4.15-rc3

Post Syndicated from corbet original https://lwn.net/Articles/741066/rss

The 4.15-rc3 kernel prepatch is out.
I’m not thrilled about how big the early 4.15 rc’s are, but rc3 is
often the biggest rc because it’s still fairly early in the
calming-down period, and yet people have had some time to start
finding problems. That said, this rc3 is big even by rc3 standards.
Not good.
” 489 changesets were merged since 4.15-rc2.

Amazon EC2 Update – Streamlined Access to Spot Capacity, Smooth Price Changes, Instance Hibernation

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/amazon-ec2-update-streamlined-access-to-spot-capacity-smooth-price-changes-instance-hibernation/

EC2 Spot Instances give you access to spare compute capacity in the AWS Cloud. Our customers use fleets of Spot Instances to power their CI/CD environments & traffic generators, host web servers & microservices, render movies, and to run many types of analytics jobs, all at prices that offer significant savings in comparison to On-Demand Instances.

New Streamlined Access
Today we are introducing a new, streamlined access model for Spot Instances. You simply indicate your desire to use Spot capacity when you launch an instance via the RunInstances function, the run-instances command, or the AWS Management Console to submit a request that will be fulfilled as long as the capacity is available. With no extra effort on your part you’ll save up to 90% off of the On-Demand price for the instance type, allowing you to boost your overall application throughput by up to 10x for the same budget. The instances that you launch in this way will continue to run until you terminate them or if EC2 needs to reclaim them for On-Demand usage. At that point the instance will be given the usual 2-minute warning and then reclaimed, making this a great fit for applications that are fault-tolerant.

Unlike the old model which required an understanding of Spot markets, bidding, and calls to a standalone asynchronous API, the new model is synchronous and as easy to use as On-Demand. Your code or your script receives an Instance ID immediately and need not check back to see if the request has been processed and accepted.

We’ve made this as clean and as simple as possible, with the expectation that it will be easy to modify many current scripts and applications to request and make use of Spot capacity. If you want to exercise additional control over your Spot instance budget, you have the option to specify a maximum price when you make a request for capacity. If you use Spot capacity to power your Amazon EMR, Amazon ECS, or AWS Batch clusters, or if you launch Spot instances by way of a AWS CloudFormation template or Auto Scaling Group, you will benefit from this new model without having to make any changes.

Applications that are built around RequestSpotInstances or RequestSpotFleet will continue to work just fine with no changes. However, you now have the option to make requests that do not include the SpotPrice parameter.

Smooth Price Changes
As part of today’s launch we are also changing the way that Spot prices change, moving to a model where prices adjust more gradually, based on longer-term trends in supply and demand. As I mentioned earlier, you will continue to save an average of 70-90% off the On-Demand price, and you will continue to pay the Spot price that’s in effect for the time period your instances are running. Applications built around our Spot Fleet feature will continue to automatically diversify placement of their Spot Instances across the most cost-effective pools based on the configuration you specified when you created the fleet.

Spot in Action
To launch a Spot Instance from the command line; simply specify the Spot market:

$ aws ec2 run-instances –-market Spot --image-id ami-1a2b3c4d --count 1 --instance-type c3.large 

Instance Hibernation
If you run workloads that keep a lot of state in memory, you will love this new feature!

You can arrange for instances to save their in-memory state when they are reclaimed, allowing the instances and the applications on them to pick up where they left off when capacity is once again available, just like closing and then opening your laptop. This feature works on C3, C4, and certain sizes of R3, R4, and M4 instances running Amazon Linux, Ubuntu, or Windows Server, and is supported by the EC2 Hibernation Agent.

The in-memory state is written to the root EBS volume of the instance using space that is set-aside when the instance launches. The private IP address and any Elastic IP Addresses are also preserved across a stop/start cycle.

Jeff;

Object models

Post Syndicated from Eevee original https://eev.ee/blog/2017/11/28/object-models/

Anonymous asks, with dollars:

More about programming languages!

Well then!

I’ve written before about what I think objects are: state and behavior, which in practice mostly means method calls.

I suspect that the popular impression of what objects are, and also how they should work, comes from whatever C++ and Java happen to do. From that point of view, the whole post above is probably nonsense. If the baseline notion of “object” is a rigid definition woven tightly into the design of two massively popular languages, then it doesn’t even make sense to talk about what “object” should mean — it does mean the features of those languages, and cannot possibly mean anything else.

I think that’s a shame! It piles a lot of baggage onto a fairly simple idea. Polymorphism, for example, has nothing to do with objects — it’s an escape hatch for static type systems. Inheritance isn’t the only way to reuse code between objects, but it’s the easiest and fastest one, so it’s what we get. Frankly, it’s much closer to a speed tradeoff than a fundamental part of the concept.

We could do with more experimentation around how objects work, but that’s impossible in the languages most commonly thought of as object-oriented.

Here, then, is a (very) brief run through the inner workings of objects in four very dynamic languages. I don’t think I really appreciated objects until I’d spent some time with Python, and I hope this can help someone else whet their own appetite.

Python 3

Of the four languages I’m going to touch on, Python will look the most familiar to the Java and C++ crowd. For starters, it actually has a class construct.

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class Vector:
    def __init__(self, x, y):
        self.x = x
        self.y = y

    def __neg__(self):
        return Vector(-self.x, -self.y)

    def __div__(self, denom):
        return Vector(self.x / denom, self.y / denom)

    @property
    def magnitude(self):
        return (self.x ** 2 + self.y ** 2) ** 0.5

    def normalized(self):
        return self / self.magnitude

The __init__ method is an initializer, which is like a constructor but named differently (because the object already exists in a usable form by the time the initializer is called). Operator overloading is done by implementing methods with other special __dunder__ names. Properties can be created with @property, where the @ is syntax for applying a wrapper function to a function as it’s defined. You can do inheritance, even multiply:

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class Foo(A, B, C):
    def bar(self, x, y, z):
        # do some stuff
        super().bar(x, y, z)

Cool, a very traditional object model.

Except… for some details.

Some details

For one, Python objects don’t have a fixed layout. Code both inside and outside the class can add or remove whatever attributes they want from whatever object they want. The underlying storage is just a dict, Python’s mapping type. (Or, rather, something like one. Also, it’s possible to change, which will probably be the case for everything I say here.)

If you create some attributes at the class level, you’ll start to get a peek behind the curtains:

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class Foo:
    values = []

    def add_value(self, value):
        self.values.append(value)

a = Foo()
b = Foo()
a.add_value('a')
print(a.values)  # ['a']
b.add_value('b')
print(b.values)  # ['a', 'b']

The [] assigned to values isn’t a default assigned to each object. In fact, the individual objects don’t know about it at all! You can use vars(a) to get at the underlying storage dict, and you won’t see a values entry in there anywhere.

Instead, values lives on the class, which is a value (and thus an object) in its own right. When Python is asked for self.values, it checks to see if self has a values attribute; in this case, it doesn’t, so Python keeps going and asks the class for one.

Python’s object model is secretly prototypical — a class acts as a prototype, as a shared set of fallback values, for its objects.

In fact, this is also how method calls work! They aren’t syntactically special at all, which you can see by separating the attribute lookup from the call.

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print("abc".startswith("a"))  # True
meth = "abc".startswith
print(meth("a"))  # True

Reading obj.method looks for a method attribute; if there isn’t one on obj, Python checks the class. Here, it finds one: it’s a function from the class body.

Ah, but wait! In the code I just showed, meth seems to “know” the object it came from, so it can’t just be a plain function. If you inspect the resulting value, it claims to be a “bound method” or “built-in method” rather than a function, too. Something funny is going on here, and that funny something is the descriptor protocol.

Descriptors

Python allows attributes to implement their own custom behavior when read from or written to. Such an attribute is called a descriptor. I’ve written about them before, but here’s a quick overview.

If Python looks up an attribute, finds it in a class, and the value it gets has a __get__ method… then instead of using that value, Python will use the return value of its __get__ method.

The @property decorator works this way. The magnitude property in my original example was shorthand for doing this:

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class MagnitudeDescriptor:
    def __get__(self, instance, owner):
        if instance is None:
            return self
        return (instance.x ** 2 + instance.y ** 2) ** 0.5

class Vector:
    def __init__(self, x, y):
        self.x = x
        self.y = y

    magnitude = MagnitudeDescriptor()

When you ask for somevec.magnitude, Python checks somevec but doesn’t find magnitude, so it consults the class instead. The class does have a magnitude, and it’s a value with a __get__ method, so Python calls that method and somevec.magnitude evaluates to its return value. (The instance is None check is because __get__ is called even if you get the descriptor directly from the class via Vector.magnitude. A descriptor intended to work on instances can’t do anything useful in that case, so the convention is to return the descriptor itself.)

You can also intercept attempts to write to or delete an attribute, and do absolutely whatever you want instead. But note that, similar to operating overloading in Python, the descriptor must be on a class; you can’t just slap one on an arbitrary object and have it work.

This brings me right around to how “bound methods” actually work. Functions are descriptors! The function type implements __get__, and when a function is retrieved from a class via an instance, that __get__ bundles the function and the instance together into a tiny bound method object. It’s essentially:

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class FunctionType:
    def __get__(self, instance, owner):
        if instance is None:
            return self
        return functools.partial(self, instance)

The self passed as the first argument to methods is not special or magical in any way. It’s built out of a few simple pieces that are also readily accessible to Python code.

Note also that because obj.method() is just an attribute lookup and a call, Python doesn’t actually care whether method is a method on the class or just some callable thing on the object. You won’t get the auto-self behavior if it’s on the object, but otherwise there’s no difference.

More attribute access, and the interesting part

Descriptors are one of several ways to customize attribute access. Classes can implement __getattr__ to intervene when an attribute isn’t found on an object; __setattr__ and __delattr__ to intervene when any attribute is set or deleted; and __getattribute__ to implement unconditional attribute access. (That last one is a fantastic way to create accidental recursion, since any attribute access you do within __getattribute__ will of course call __getattribute__ again.)

Here’s what I really love about Python. It might seem like a magical special case that descriptors only work on classes, but it really isn’t. You could implement exactly the same behavior yourself, in pure Python, using only the things I’ve just told you about. Classes are themselves objects, remember, and they are instances of type, so the reason descriptors only work on classes is that type effectively does this:

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class type:
    def __getattribute__(self, name):
        value = super().__getattribute__(name)
        # like all op overloads, __get__ must be on the type, not the instance
        ty = type(value)
        if hasattr(ty, '__get__'):
            # it's a descriptor!  this is a class access so there is no instance
            return ty.__get__(value, None, self)
        else:
            return value

You can even trivially prove to yourself that this is what’s going on by skipping over types behavior:

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class Descriptor:
    def __get__(self, instance, owner):
        print('called!')

class Foo:
    bar = Descriptor()

Foo.bar  # called!
type.__getattribute__(Foo, 'bar')  # called!
object.__getattribute__(Foo, 'bar')  # ...

And that’s not all! The mysterious super function, used to exhaustively traverse superclass method calls even in the face of diamond inheritance, can also be expressed in pure Python using these primitives. You could write your own superclass calling convention and use it exactly the same way as super.

This is one of the things I really like about Python. Very little of it is truly magical; virtually everything about the object model exists in the types rather than the language, which means virtually everything can be customized in pure Python.

Class creation and metaclasses

A very brief word on all of this stuff, since I could talk forever about Python and I have three other languages to get to.

The class block itself is fairly interesting. It looks like this:

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class Name(*bases, **kwargs):
    # code

I’ve said several times that classes are objects, and in fact the class block is one big pile of syntactic sugar for calling type(...) with some arguments to create a new type object.

The Python documentation has a remarkably detailed description of this process, but the gist is:

  • Python determines the type of the new class — the metaclass — by looking for a metaclass keyword argument. If there isn’t one, Python uses the “lowest” type among the provided base classes. (If you’re not doing anything special, that’ll just be type, since every class inherits from object and object is an instance of type.)

  • Python executes the class body. It gets its own local scope, and any assignments or method definitions go into that scope.

  • Python now calls type(name, bases, attrs, **kwargs). The name is whatever was right after class; the bases are position arguments; and attrs is the class body’s local scope. (This is how methods and other class attributes end up on the class.) The brand new type is then assigned to Name.

Of course, you can mess with most of this. You can implement __prepare__ on a metaclass, for example, to use a custom mapping as storage for the local scope — including any reads, which allows for some interesting shenanigans. The only part you can’t really implement in pure Python is the scoping bit, which has a couple extra rules that make sense for classes. (In particular, functions defined within a class block don’t close over the class body; that would be nonsense.)

Object creation

Finally, there’s what actually happens when you create an object — including a class, which remember is just an invocation of type(...).

Calling Foo(...) is implemented as, well, a call. Any type can implement calls with the __call__ special method, and you’ll find that type itself does so. It looks something like this:

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# oh, a fun wrinkle that's hard to express in pure python: type is a class, so
# it's an instance of itself
class type:
    def __call__(self, *args, **kwargs):
        # remember, here 'self' is a CLASS, an instance of type.
        # __new__ is a true constructor: object.__new__ allocates storage
        # for a new blank object
        instance = self.__new__(self, *args, **kwargs)
        # you can return whatever you want from __new__ (!), and __init__
        # is only called on it if it's of the right type
        if isinstance(instance, self):
            instance.__init__(*args, **kwargs)
        return instance

Again, you can trivially confirm this by asking any type for its __call__ method. Assuming that type doesn’t implement __call__ itself, you’ll get back a bound version of types implementation.

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>>> list.__call__
<method-wrapper '__call__' of type object at 0x7fafb831a400>

You can thus implement __call__ in your own metaclass to completely change how subclasses are created — including skipping the creation altogether, if you like.

And… there’s a bunch of stuff I haven’t even touched on.

The Python philosophy

Python offers something that, on the surface, looks like a “traditional” class/object model. Under the hood, it acts more like a prototypical system, where failed attribute lookups simply defer to a superclass or metaclass.

The language also goes to almost superhuman lengths to expose all of its moving parts. Even the prototypical behavior is an implementation of __getattribute__ somewhere, which you are free to completely replace in your own types. Proxying and delegation are easy.

Also very nice is that these features “bundle” well, by which I mean a library author can do all manner of convoluted hijinks, and a consumer of that library doesn’t have to see any of it or understand how it works. You only need to inherit from a particular class (which has a metaclass), or use some descriptor as a decorator, or even learn any new syntax.

This meshes well with Python culture, which is pretty big on the principle of least surprise. These super-advanced features tend to be tightly confined to single simple features (like “makes a weak attribute“) or cordoned with DSLs (e.g., defining a form/struct/database table with a class body). In particular, I’ve never seen a metaclass in the wild implement its own __call__.

I have mixed feelings about that. It’s probably a good thing overall that the Python world shows such restraint, but I wonder if there are some very interesting possibilities we’re missing out on. I implemented a metaclass __call__ myself, just once, in an entity/component system that strove to minimize fuss when communicating between components. It never saw the light of day, but I enjoyed seeing some new things Python could do with the same relatively simple syntax. I wouldn’t mind seeing, say, an object model based on composition (with no inheritance) built atop Python’s primitives.

Lua

Lua doesn’t have an object model. Instead, it gives you a handful of very small primitives for building your own object model. This is pretty typical of Lua — it’s a very powerful language, but has been carefully constructed to be very small at the same time. I’ve never encountered anything else quite like it, and “but it starts indexing at 1!” really doesn’t do it justice.

The best way to demonstrate how objects work in Lua is to build some from scratch. We need two key features. The first is metatables, which bear a passing resemblance to Python’s metaclasses.

Tables and metatables

The table is Lua’s mapping type and its primary data structure. Keys can be any value other than nil. Lists are implemented as tables whose keys are consecutive integers starting from 1. Nothing terribly surprising. The dot operator is sugar for indexing with a string key.

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local t = { a = 1, b = 2 }
print(t['a'])  -- 1
print(t.b)  -- 2
t.c = 3
print(t['c'])  -- 3

A metatable is a table that can be associated with another value (usually another table) to change its behavior. For example, operator overloading is implemented by assigning a function to a special key in a metatable.

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local t = { a = 1, b = 2 }
--print(t + 0)  -- error: attempt to perform arithmetic on a table value

local mt = {
    __add = function(left, right)
        return 12
    end,
}
setmetatable(t, mt)
print(t + 0)  -- 12

Now, the interesting part: one of the special keys is __index, which is consulted when the base table is indexed by a key it doesn’t contain. Here’s a table that claims every key maps to itself.

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local t = {}
local mt = {
    __index = function(table, key)
        return key
    end,
}
setmetatable(t, mt)
print(t.foo)  -- foo
print(t.bar)  -- bar
print(t[3])  -- 3

__index doesn’t have to be a function, either. It can be yet another table, in which case that table is simply indexed with the key. If the key still doesn’t exist and that table has a metatable with an __index, the process repeats.

With this, it’s easy to have several unrelated tables that act as a single table. Call the base table an object, fill the __index table with functions and call it a class, and you have half of an object system. You can even get prototypical inheritance by chaining __indexes together.

At this point things are a little confusing, since we have at least three tables going on, so here’s a diagram. Keep in mind that Lua doesn’t actually have anything called an “object”, “class”, or “method” — those are just convenient nicknames for a particular structure we might build with Lua’s primitives.

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                    ╔═══════════╗        ...
                    ║ metatable ║         ║
                    ╟───────────╢   ┌─────╨───────────────────────┐
                    ║ __index   ╫───┤ lookup table ("superclass") │
                    ╚═══╦═══════╝   ├─────────────────────────────┤
  ╔═══════════╗         ║           │ some other method           ┼─── function() ... end
  ║ metatable ║         ║           └─────────────────────────────┘
  ╟───────────╢   ┌─────╨──────────────────┐
  ║ __index   ╫───┤ lookup table ("class") │
  ╚═══╦═══════╝   ├────────────────────────┤
      ║           │ some method            ┼─── function() ... end
      ║           └────────────────────────┘
┌─────╨─────────────────┐
│ base table ("object") │
└───────────────────────┘

Note that a metatable is not the same as a class; it defines behavior, not methods. Conversely, if you try to use a class directly as a metatable, it will probably not do much. (This is pretty different from e.g. Python, where operator overloads are just methods with funny names. One nice thing about the Lua approach is that you can keep interface-like functionality separate from methods, and avoid clogging up arbitrary objects’ namespaces. You could even use a dummy table as a key and completely avoid name collisions.)

Anyway, code!

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local class = {
    foo = function(a)
        print("foo got", a)
    end,
}
local mt = { __index = class }
-- setmetatable returns its first argument, so this is nice shorthand
local obj1 = setmetatable({}, mt)
local obj2 = setmetatable({}, mt)
obj1.foo(7)  -- foo got 7
obj2.foo(9)  -- foo got 9

Wait, wait, hang on. Didn’t I call these methods? How do they get at the object? Maybe Lua has a magical this variable?

Methods, sort of

Not quite, but this is where the other key feature comes in: method-call syntax. It’s the lightest touch of sugar, just enough to have method invocation.

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-- note the colon!
a:b(c, d, ...)

-- exactly equivalent to this
-- (except that `a` is only evaluated once)
a.b(a, c, d, ...)

-- which of course is really this
a["b"](a, c, d, ...)

Now we can write methods that actually do something.

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local class = {
    bar = function(self)
        print("our score is", self.score)
    end,
}
local mt = { __index = class }
local obj1 = setmetatable({ score = 13 }, mt)
local obj2 = setmetatable({ score = 25 }, mt)
obj1:bar()  -- our score is 13
obj2:bar()  -- our score is 25

And that’s all you need. Much like Python, methods and data live in the same namespace, and Lua doesn’t care whether obj:method() finds a function on obj or gets one from the metatable’s __index. Unlike Python, the function will be passed self either way, because self comes from the use of : rather than from the lookup behavior.

(Aside: strictly speaking, any Lua value can have a metatable — and if you try to index a non-table, Lua will always consult the metatable’s __index. Strings all have the string library as a metatable, so you can call methods on them: try ("%s %s"):format(1, 2). I don’t think Lua lets user code set the metatable for non-tables, so this isn’t that interesting, but if you’re writing Lua bindings from C then you can wrap your pointers in metatables to give them methods implemented in C.)

Bringing it all together

Of course, writing all this stuff every time is a little tedious and error-prone, so instead you might want to wrap it all up inside a little function. No problem.

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local function make_object(body)
    -- create a metatable
    local mt = { __index = body }
    -- create a base table to serve as the object itself
    local obj = setmetatable({}, mt)
    -- and, done
    return obj
end

-- you can leave off parens if you're only passing in 
local Dog = {
    -- this acts as a "default" value; if obj.barks is missing, __index will
    -- kick in and find this value on the class.  but if obj.barks is assigned
    -- to, it'll go in the object and shadow the value here.
    barks = 0,

    bark = function(self)
        self.barks = self.barks + 1
        print("woof!")
    end,
}

local mydog = make_object(Dog)
mydog:bark()  -- woof!
mydog:bark()  -- woof!
mydog:bark()  -- woof!
print(mydog.barks)  -- 3
print(Dog.barks)  -- 0

It works, but it’s fairly barebones. The nice thing is that you can extend it pretty much however you want. I won’t reproduce an entire serious object system here — lord knows there are enough of them floating around — but the implementation I have for my LÖVE games lets me do this:

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local Animal = Object:extend{
    cries = 0,
}

-- called automatically by Object
function Animal:init()
    print("whoops i couldn't think of anything interesting to put here")
end

-- this is just nice syntax for adding a first argument called 'self', then
-- assigning this function to Animal.cry
function Animal:cry()
    self.cries = self.cries + 1
end

local Cat = Animal:extend{}

function Cat:cry()
    print("meow!")
    Cat.__super.cry(self)
end

local cat = Cat()
cat:cry()  -- meow!
cat:cry()  -- meow!
print(cat.cries)  -- 2

When I say you can extend it however you want, I mean that. I could’ve implemented Python (2)-style super(Cat, self):cry() syntax; I just never got around to it. I could even make it work with multiple inheritance if I really wanted to — or I could go the complete opposite direction and only implement composition. I could implement descriptors, customizing the behavior of individual table keys. I could add pretty decent syntax for composition/proxying. I am trying very hard to end this section now.

The Lua philosophy

Lua’s philosophy is to… not have a philosophy? It gives you the bare minimum to make objects work, and you can do absolutely whatever you want from there. Lua does have something resembling prototypical inheritance, but it’s not so much a first-class feature as an emergent property of some very simple tools. And since you can make __index be a function, you could avoid the prototypical behavior and do something different entirely.

The very severe downside, of course, is that you have to find or build your own object system — which can get pretty confusing very quickly, what with the multiple small moving parts. Third-party code may also have its own object system with subtly different behavior. (Though, in my experience, third-party code tries very hard to avoid needing an object system at all.)

It’s hard to say what the Lua “culture” is like, since Lua is an embedded language that’s often a little different in each environment. I imagine it has a thousand millicultures, instead. I can say that the tedium of building my own object model has led me into something very “traditional”, with prototypical inheritance and whatnot. It’s partly what I’m used to, but it’s also just really dang easy to get working.

Likewise, while I love properties in Python and use them all the dang time, I’ve yet to use a single one in Lua. They wouldn’t be particularly hard to add to my object model, but having to add them myself (or shop around for an object model with them and also port all my code to use it) adds a huge amount of friction. I’ve thought about designing an interesting ECS with custom object behavior, too, but… is it really worth the effort? For all the power and flexibility Lua offers, the cost is that by the time I have something working at all, I’m too exhausted to actually use any of it.

JavaScript

JavaScript is notable for being preposterously heavily used, yet not having a class block.

Well. Okay. Yes. It has one now. It didn’t for a very long time, and even the one it has now is sugar.

Here’s a vector class again:

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class Vector {
    constructor(x, y) {
        this.x = x;
        this.y = y;
    }

    get magnitude() {
        return Math.sqrt(this.x * this.x + this.y * this.y);
    }

    dot(other) {
        return this.x * other.x + this.y * other.y;
    }
}

In “classic” JavaScript, this would be written as:

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function Vector(x, y) {
    this.x = x;
    this.y = y;
}

Object.defineProperty(Vector.prototype, 'magnitude', {
    configurable: true,
    enumerable: true,
    get: function() {
        return Math.sqrt(this.x * this.x + this.y * this.y);
    },
});


Vector.prototype.dot = function(other) {
    return this.x * other.x + this.y * other.y;
};

Hm, yes. I can see why they added class.

The JavaScript model

In JavaScript, a new type is defined in terms of a function, which is its constructor.

Right away we get into trouble here. There is a very big difference between these two invocations, which I actually completely forgot about just now after spending four hours writing about Python and Lua:

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let vec = Vector(3, 4);
let vec = new Vector(3, 4);

The first calls the function Vector. It assigns some properties to this, which here is going to be window, so now you have a global x and y. It then returns nothing, so vec is undefined.

The second calls Vector with this set to a new empty object, then evaluates to that object. The result is what you’d actually expect.

(You can detect this situation with the strange new.target expression, but I have never once remembered to do so.)

From here, we have true, honest-to-god, first-class prototypical inheritance. The word “prototype” is even right there. When you write this:

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vec.dot(vec2)

JavaScript will look for dot on vec and (presumably) not find it. It then consults vecs prototype, an object you can see for yourself by using Object.getPrototypeOf(). Since vec is a Vector, its prototype is Vector.prototype.

I stress that Vector.prototype is not the prototype for Vector. It’s the prototype for instances of Vector.

(I say “instance”, but the true type of vec here is still just object. If you want to find Vector, it’s automatically assigned to the constructor property of its own prototype, so it’s available as vec.constructor.)

Of course, Vector.prototype can itself have a prototype, in which case the process would continue if dot were not found. A common (and, arguably, very bad) way to simulate single inheritance is to set Class.prototype to an instance of a superclass to get the prototype right, then tack on the methods for Class. Nowadays we can do Object.create(Superclass.prototype).

Now that I’ve been through Python and Lua, though, this isn’t particularly surprising. I kinda spoiled it.

I suppose one difference in JavaScript is that you can tack arbitrary attributes directly onto Vector all you like, and they will remain invisible to instances since they aren’t in the prototype chain. This is kind of backwards from Lua, where you can squirrel stuff away in the metatable.

Another difference is that every single object in JavaScript has a bunch of properties already tacked on — the ones in Object.prototype. Every object (and by “object” I mean any mapping) has a prototype, and that prototype defaults to Object.prototype, and it has a bunch of ancient junk like isPrototypeOf.

(Nit: it’s possible to explicitly create an object with no prototype via Object.create(null).)

Like Lua, and unlike Python, JavaScript doesn’t distinguish between keys found on an object and keys found via a prototype. Properties can be defined on prototypes with Object.defineProperty(), but that works just as well directly on an object, too. JavaScript doesn’t have a lot of operator overloading, but some things like Symbol.iterator also work on both objects and prototypes.

About this

You may, at this point, be wondering what this is. Unlike Lua and Python (and the last language below), this is a special built-in value — a context value, invisibly passed for every function call.

It’s determined by where the function came from. If the function was the result of an attribute lookup, then this is set to the object containing that attribute. Otherwise, this is set to the global object, window. (You can also set this to whatever you want via the call method on functions.)

This decision is made lexically, i.e. from the literal source code as written. There are no Python-style bound methods. In other words:

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// this = obj
obj.method()
// this = window
let meth = obj.method
meth()

Also, because this is reassigned on every function call, it cannot be meaningfully closed over, which makes using closures within methods incredibly annoying. The old approach was to assign this to some other regular name like self (which got syntax highlighting since it’s also a built-in name in browsers); then we got Function.bind, which produced a callable thing with a fixed context value, which was kind of nice; and now finally we have arrow functions, which explicitly close over the current this when they’re defined and don’t change it when called. Phew.

Class syntax

I already showed class syntax, and it’s really just one big macro for doing all the prototype stuff The Right Way. It even prevents you from calling the type without new. The underlying model is exactly the same, and you can inspect all the parts.

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class Vector { ... }

console.log(Vector.prototype);  // { dot: ..., magnitude: ..., ... }
let vec = new Vector(3, 4);
console.log(Object.getPrototypeOf(vec));  // same as Vector.prototype

// i don't know why you would subclass vector but let's roll with it
class Vectest extends Vector { ... }

console.log(Vectest.prototype);  // { ... }
console.log(Object.getPrototypeOf(Vectest.prototype))  // same as Vector.prototype

Alas, class syntax has a couple shortcomings. You can’t use the class block to assign arbitrary data to either the type object or the prototype — apparently it was deemed too confusing that mutations would be shared among instances. Which… is… how prototypes work. How Python works. How JavaScript itself, one of the most popular languages of all time, has worked for twenty-two years. Argh.

You can still do whatever assignment you want outside of the class block, of course. It’s just a little ugly, and not something I’d think to look for with a sugary class.

A more subtle result of this behavior is that a class block isn’t quite the same syntax as an object literal. The check for data isn’t a runtime thing; class Foo { x: 3 } fails to parse. So JavaScript now has two largely but not entirely identical styles of key/value block.

Attribute access

Here’s where things start to come apart at the seams, just a little bit.

JavaScript doesn’t really have an attribute protocol. Instead, it has two… extension points, I suppose.

One is Object.defineProperty, seen above. For common cases, there’s also the get syntax inside a property literal, which does the same thing. But unlike Python’s @property, these aren’t wrappers around some simple primitives; they are the primitives. JavaScript is the only language of these four to have “property that runs code on access” as a completely separate first-class concept.

If you want to intercept arbitrary attribute access (and some kinds of operators), there’s a completely different primitive: the Proxy type. It doesn’t let you intercept attribute access or operators; instead, it produces a wrapper object that supports interception and defers to the wrapped object by default.

It’s cool to see composition used in this way, but also, extremely weird. If you want to make your own type that overloads in or calling, you have to return a Proxy that wraps your own type, rather than actually returning your own type. And (unlike the other three languages in this post) you can’t return a different type from a constructor, so you have to throw that away and produce objects only from a factory. And instanceof would be broken, but you can at least fix that with Symbol.hasInstance — which is really operator overloading, implement yet another completely different way.

I know the design here is a result of legacy and speed — if any object could intercept all attribute access, then all attribute access would be slowed down everywhere. Fair enough. It still leaves the surface area of the language a bit… bumpy?

The JavaScript philosophy

It’s a little hard to tell. The original idea of prototypes was interesting, but it was hidden behind some very awkward syntax. Since then, we’ve gotten a bunch of extra features awkwardly bolted on to reflect the wildly varied things the built-in types and DOM API were already doing. We have class syntax, but it’s been explicitly designed to avoid exposing the prototype parts of the model.

I admit I don’t do a lot of heavy JavaScript, so I might just be overlooking it, but I’ve seen virtually no code that makes use of any of the recent advances in object capabilities. Forget about custom iterators or overloading call; I can’t remember seeing any JavaScript in the wild that even uses properties yet. I don’t know if everyone’s waiting for sufficient browser support, nobody knows about them, or nobody cares.

The model has advanced recently, but I suspect JavaScript is still shackled to its legacy of “something about prototypes, I don’t really get it, just copy the other code that’s there” as an object model. Alas! Prototypes are so good. Hopefully class syntax will make it a bit more accessible, as it has in Python.

Perl 5

Perl 5 also doesn’t have an object system and expects you to build your own. But where Lua gives you two simple, powerful tools for building one, Perl 5 feels more like a puzzle with half the pieces missing. Clearly they were going for something, but they only gave you half of it.

In brief, a Perl object is a reference that has been blessed with a package.

I need to explain a few things. Honestly, one of the biggest problems with the original Perl object setup was how many strange corners and unique jargon you had to understand just to get off the ground.

(If you want to try running any of this code, you should stick a use v5.26; as the first line. Perl is very big on backwards compatibility, so you need to opt into breaking changes, and even the mundane say builtin is behind a feature gate.)

References

A reference in Perl is sort of like a pointer, but its main use is very different. See, Perl has the strange property that its data structures try very hard to spill their contents all over the place. Despite having dedicated syntax for arrays — @foo is an array variable, distinct from the single scalar variable $foo — it’s actually impossible to nest arrays.

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my @foo = (1, 2, 3, 4);
my @bar = (@foo, @foo);
# @bar is now a flat list of eight items: 1, 2, 3, 4, 1, 2, 3, 4

The idea, I guess, is that an array is not one thing. It’s not a container, which happens to hold multiple things; it is multiple things. Anywhere that expects a single value, such as an array element, cannot contain an array, because an array fundamentally is not a single value.

And so we have “references”, which are a form of indirection, but also have the nice property that they’re single values. They add containment around arrays, and in general they make working with most of Perl’s primitive types much more sensible. A reference to a variable can be taken with the \ operator, or you can use [ ... ] and { ... } to directly create references to anonymous arrays or hashes.

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my @foo = (1, 2, 3, 4);
my @bar = (\@foo, \@foo);
# @bar is now a nested list of two items: [1, 2, 3, 4], [1, 2, 3, 4]

(Incidentally, this is the sole reason I initially abandoned Perl for Python. Non-trivial software kinda requires nesting a lot of data structures, so you end up with references everywhere, and the syntax for going back and forth between a reference and its contents is tedious and ugly.)

A Perl object must be a reference. Perl doesn’t care what kind of reference — it’s usually a hash reference, since hashes are a convenient place to store arbitrary properties, but it could just as well be a reference to an array, a scalar, or even a sub (i.e. function) or filehandle.

I’m getting a little ahead of myself. First, the other half: blessing and packages.

Packages and blessing

Perl packages are just namespaces. A package looks like this:

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package Foo::Bar;

sub quux {
    say "hi from quux!";
}

# now Foo::Bar::quux() can be called from anywhere

Nothing shocking, right? It’s just a named container. A lot of the details are kind of weird, like how a package exists in some liminal quasi-value space, but the basic idea is a Bag Of Stuff.

The final piece is “blessing,” which is Perl’s funny name for binding a package to a reference. A very basic class might look like this:

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package Vector;

# the name 'new' is convention, not special
sub new {
    # perl argument passing is weird, don't ask
    my ($class, $x, $y) = @_;

    # create the object itself -- here, unusually, an array reference makes sense
    my $self = [ $x, $y ];

    # associate the package with that reference
    # note that $class here is just the regular string, 'Vector'
    bless $self, $class;

    return $self;
}

sub x {
    my ($self) = @_;
    return $self->[0];
}

sub y {
    my ($self) = @_;
    return $self->[1];
}

sub magnitude {
    my ($self) = @_;
    return sqrt($self->x ** 2 + $self->y ** 2);
}

# switch back to the "default" package
package main;

# -> is method call syntax, which passes the invocant as the first argument;
# for a package, that's just the package name
my $vec = Vector->new(3, 4);
say $vec->magnitude;  # 5

A few things of note here. First, $self->[0] has nothing to do with objects; it’s normal syntax for getting the value of a index 0 out of an array reference called $self. (Most classes are based on hashrefs and would use $self->{value} instead.) A blessed reference is still a reference and can be treated like one.

In general, -> is Perl’s dereferencey operator, but its exact behavior depends on what follows. If it’s followed by brackets, then it’ll apply the brackets to the thing in the reference: ->{} to index a hash reference, ->[] to index an array reference, and ->() to call a function reference.

But if -> is followed by an identifier, then it’s a method call. For packages, that means calling a function in the package and passing the package name as the first argument. For objects — blessed references — that means calling a function in the associated package and passing the object as the first argument.

This is a little weird! A blessed reference is a superposition of two things: its normal reference behavior, and some completely orthogonal object behavior. Also, object behavior has no notion of methods vs data; it only knows about methods. Perl lets you omit parentheses in a lot of places, including when calling a method with no arguments, so $vec->magnitude is really $vec->magnitude().

Perl’s blessing bears some similarities to Lua’s metatables, but ultimately Perl is much closer to Ruby’s “message passing” approach than the above three languages’ approaches of “get me something and maybe it’ll be callable”. (But this is no surprise — Ruby is a spiritual successor to Perl 5.)

All of this leads to one little wrinkle: how do you actually expose data? Above, I had to write x and y methods. Am I supposed to do that for every single attribute on my type?

Yes! But don’t worry, there are third-party modules to help with this incredibly fundamental task. Take Class::Accessor::Fast, so named because it’s faster than Class::Accessor:

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package Foo;
use base qw(Class::Accessor::Fast);
__PACKAGE__->mk_accessors(qw(fred wilma barney));

(__PACKAGE__ is the lexical name of the current package; qw(...) is a list literal that splits its contents on whitespace.)

This assumes you’re using a hashref with keys of the same names as the attributes. $obj->fred will return the fred key from your hashref, and $obj->fred(4) will change it to 4.

You also, somewhat bizarrely, have to inherit from Class::Accessor::Fast. Speaking of which,

Inheritance

Inheritance is done by populating the package-global @ISA array with some number of (string) names of parent packages. Most code instead opts to write use base ...;, which does the same thing. Or, more commonly, use parent ...;, which… also… does the same thing.

Every package implicitly inherits from UNIVERSAL, which can be freely modified by Perl code.

A method can call its superclass method with the SUPER:: pseudo-package:

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sub foo {
    my ($self) = @_;
    $self->SUPER::foo;
}

However, this does a depth-first search, which means it almost certainly does the wrong thing when faced with multiple inheritance. For a while the accepted solution involved a third-party module, but Perl eventually grew an alternative you have to opt into: C3, which may be more familiar to you as the order Python uses.

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use mro 'c3';

sub foo {
    my ($self) = @_;
    $self->next::method;
}

Offhand, I’m not actually sure how next::method works, seeing as it was originally implemented in pure Perl code. I suspect it involves peeking at the caller’s stack frame. If so, then this is a very different style of customizability from e.g. Python — the MRO was never intended to be pluggable, and the use of a special pseudo-package means it isn’t really, but someone was determined enough to make it happen anyway.

Operator overloading and whatnot

Operator overloading looks a little weird, though really it’s pretty standard Perl.

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package MyClass;

use overload '+' => \&_add;

sub _add {
    my ($self, $other, $swap) = @_;
    ...
}

use overload here is a pragma, where “pragma” means “regular-ass module that does some wizardry when imported”.

\&_add is how you get a reference to the _add sub so you can pass it to the overload module. If you just said &_add or _add, that would call it.

And that’s it; you just pass a map of operators to functions to this built-in module. No worry about name clashes or pollution, which is pretty nice. You don’t even have to give references to functions that live in the package, if you don’t want them to clog your namespace; you could put them in another package, or even inline them anonymously.

One especially interesting thing is that Perl lets you overload every operator. Perl has a lot of operators. It considers some math builtins like sqrt and trig functions to be operators, or at least operator-y enough that you can overload them. You can also overload the “file text” operators, such as -e $path to test whether a file exists. You can overload conversions, including implicit conversion to a regex. And most fascinating to me, you can overload dereferencing — that is, the thing Perl does when you say $hashref->{key} to get at the underlying hash. So a single object could pretend to be references of multiple different types, including a subref to implement callability. Neat.

Somewhat related: you can overload basic operators (indexing, etc.) on basic types (not references!) with the tie function, which is designed completely differently and looks for methods with fixed names. Go figure.

You can intercept calls to nonexistent methods by implementing a function called AUTOLOAD, within which the $AUTOLOAD global will contain the name of the method being called. Originally this feature was, I think, intended for loading binary components or large libraries on-the-fly only when needed, hence the name. Offhand I’m not sure I ever saw it used the way __getattr__ is used in Python.

Is there a way to intercept all method calls? I don’t think so, but it is Perl, so I must be forgetting something.

Actually no one does this any more

Like a decade ago, a council of elder sages sat down and put together a whole whizbang system that covers all of it: Moose.

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package Vector;
use Moose;

has x => (is => 'rw', isa => 'Int');
has y => (is => 'rw', isa => 'Int');

sub magnitude {
    my ($self) = @_;
    return sqrt($self->x ** 2 + $self->y ** 2);
}

Moose has its own way to do pretty much everything, and it’s all built on the same primitives. Moose also adds metaclasses, somehow, despite that the underlying model doesn’t actually support them? I’m not entirely sure how they managed that, but I do remember doing some class introspection with Moose and it was much nicer than the built-in way.

(If you’re wondering, the built-in way begins with looking at the hash called %Vector::. No, that’s not a typo.)

I really cannot stress enough just how much stuff Moose does, but I don’t want to delve into it here since Moose itself is not actually the language model.

The Perl philosophy

I hope you can see what I meant with what I first said about Perl, now. It has multiple inheritance with an MRO, but uses the wrong one by default. It has extensive operator overloading, which looks nothing like how inheritance works, and also some of it uses a totally different mechanism with special method names instead. It only understands methods, not data, leaving you to figure out accessors by hand.

There’s 70% of an object system here with a clear general design it was gunning for, but none of the pieces really look anything like each other. It’s weird, in a distinctly Perl way.

The result is certainly flexible, at least! It’s especially cool that you can use whatever kind of reference you want for storage, though even as I say that, I acknowledge it’s no different from simply subclassing list or something in Python. It feels different in Perl, but maybe only because it looks so different.

I haven’t written much Perl in a long time, so I don’t know what the community is like any more. Moose was already ubiquitous when I left, which you’d think would let me say “the community mostly focuses on the stuff Moose can do” — but even a decade ago, Moose could already do far more than I had ever seen done by hand in Perl. It’s always made a big deal out of roles (read: interfaces), for instance, despite that I’d never seen anyone care about them in Perl before Moose came along. Maybe their presence in Moose has made them more popular? Who knows.

Also, I wrote Perl seriously, but in the intervening years I’ve only encountered people who only ever used Perl for one-offs. Maybe it’ll come as a surprise to a lot of readers that Perl has an object model at all.

End

Well, that was fun! I hope any of that made sense.

Special mention goes to Rust, which doesn’t have an object model you can fiddle with at runtime, but does do things a little differently.

It’s been really interesting thinking about how tiny differences make a huge impact on what people do in practice. Take the choice of storage in Perl versus Python. Perl’s massively common URI class uses a string as the storage, nothing else; I haven’t seen anything like that in Python aside from markupsafe, which is specifically designed as a string type. I would guess this is partly because Perl makes you choose — using a hashref is an obvious default, but you have to make that choice one way or the other. In Python (especially 3), inheriting from object and getting dict-based storage is the obvious thing to do; the ability to use another type isn’t quite so obvious, and doing it “right” involves a tiny bit of extra work.

Or, consider that Lua could have descriptors, but the extra bit of work (especially design work) has been enough of an impediment that I’ve never implemented them. I don’t think the object implementations I’ve looked at have included them, either. Super weird!

In that light, it’s only natural that objects would be so strongly associated with the features Java and C++ attach to them. I think that makes it all the more important to play around! Look at what Moose has done. No, really, you should bear in mind my description of how Perl does stuff and flip through the Moose documentation. It’s amazing what they’ve built.

Using Amazon Redshift Spectrum, Amazon Athena, and AWS Glue with Node.js in Production

Post Syndicated from Rafi Ton original https://aws.amazon.com/blogs/big-data/using-amazon-redshift-spectrum-amazon-athena-and-aws-glue-with-node-js-in-production/

This is a guest post by Rafi Ton, founder and CEO of NUVIAD. NUVIAD is, in their own words, “a mobile marketing platform providing professional marketers, agencies and local businesses state of the art tools to promote their products and services through hyper targeting, big data analytics and advanced machine learning tools.”

At NUVIAD, we’ve been using Amazon Redshift as our main data warehouse solution for more than 3 years.

We store massive amounts of ad transaction data that our users and partners analyze to determine ad campaign strategies. When running real-time bidding (RTB) campaigns in large scale, data freshness is critical so that our users can respond rapidly to changes in campaign performance. We chose Amazon Redshift because of its simplicity, scalability, performance, and ability to load new data in near real time.

Over the past three years, our customer base grew significantly and so did our data. We saw our Amazon Redshift cluster grow from three nodes to 65 nodes. To balance cost and analytics performance, we looked for a way to store large amounts of less-frequently analyzed data at a lower cost. Yet, we still wanted to have the data immediately available for user queries and to meet their expectations for fast performance. We turned to Amazon Redshift Spectrum.

In this post, I explain the reasons why we extended Amazon Redshift with Redshift Spectrum as our modern data warehouse. I cover how our data growth and the need to balance cost and performance led us to adopt Redshift Spectrum. I also share key performance metrics in our environment, and discuss the additional AWS services that provide a scalable and fast environment, with data available for immediate querying by our growing user base.

Amazon Redshift as our foundation

The ability to provide fresh, up-to-the-minute data to our customers and partners was always a main goal with our platform. We saw other solutions provide data that was a few hours old, but this was not good enough for us. We insisted on providing the freshest data possible. For us, that meant loading Amazon Redshift in frequent micro batches and allowing our customers to query Amazon Redshift directly to get results in near real time.

The benefits were immediately evident. Our customers could see how their campaigns performed faster than with other solutions, and react sooner to the ever-changing media supply pricing and availability. They were very happy.

However, this approach required Amazon Redshift to store a lot of data for long periods, and our data grew substantially. In our peak, we maintained a cluster running 65 DC1.large nodes. The impact on our Amazon Redshift cluster was evident, and we saw our CPU utilization grow to 90%.

Why we extended Amazon Redshift to Redshift Spectrum

Redshift Spectrum gives us the ability to run SQL queries using the powerful Amazon Redshift query engine against data stored in Amazon S3, without needing to load the data. With Redshift Spectrum, we store data where we want, at the cost that we want. We have the data available for analytics when our users need it with the performance they expect.

Seamless scalability, high performance, and unlimited concurrency

Scaling Redshift Spectrum is a simple process. First, it allows us to leverage Amazon S3 as the storage engine and get practically unlimited data capacity.

Second, if we need more compute power, we can leverage Redshift Spectrum’s distributed compute engine over thousands of nodes to provide superior performance – perfect for complex queries running against massive amounts of data.

Third, all Redshift Spectrum clusters access the same data catalog so that we don’t have to worry about data migration at all, making scaling effortless and seamless.

Lastly, since Redshift Spectrum distributes queries across potentially thousands of nodes, they are not affected by other queries, providing much more stable performance and unlimited concurrency.

Keeping it SQL

Redshift Spectrum uses the same query engine as Amazon Redshift. This means that we did not need to change our BI tools or query syntax, whether we used complex queries across a single table or joins across multiple tables.

An interesting capability introduced recently is the ability to create a view that spans both Amazon Redshift and Redshift Spectrum external tables. With this feature, you can query frequently accessed data in your Amazon Redshift cluster and less-frequently accessed data in Amazon S3, using a single view.

Leveraging Parquet for higher performance

Parquet is a columnar data format that provides superior performance and allows Redshift Spectrum (or Amazon Athena) to scan significantly less data. With less I/O, queries run faster and we pay less per query. You can read all about Parquet at https://parquet.apache.org/ or https://en.wikipedia.org/wiki/Apache_Parquet.

Lower cost

From a cost perspective, we pay standard rates for our data in Amazon S3, and only small amounts per query to analyze data with Redshift Spectrum. Using the Parquet format, we can significantly reduce the amount of data scanned. Our costs are now lower, and our users get fast results even for large complex queries.

What we learned about Amazon Redshift vs. Redshift Spectrum performance

When we first started looking at Redshift Spectrum, we wanted to put it to the test. We wanted to know how it would compare to Amazon Redshift, so we looked at two key questions:

  1. What is the performance difference between Amazon Redshift and Redshift Spectrum on simple and complex queries?
  2. Does the data format impact performance?

During the migration phase, we had our dataset stored in Amazon Redshift and S3 as CSV/GZIP and as Parquet file formats. We tested three configurations:

  • Amazon Redshift cluster with 28 DC1.large nodes
  • Redshift Spectrum using CSV/GZIP
  • Redshift Spectrum using Parquet

We performed benchmarks for simple and complex queries on one month’s worth of data. We tested how much time it took to perform the query, and how consistent the results were when running the same query multiple times. The data we used for the tests was already partitioned by date and hour. Properly partitioning the data improves performance significantly and reduces query times.

Simple query

First, we tested a simple query aggregating billing data across a month:

SELECT 
  user_id, 
  count(*) AS impressions, 
  SUM(billing)::decimal /1000000 AS billing 
FROM <table_name> 
WHERE 
  date >= '2017-08-01' AND 
  date <= '2017-08-31'  
GROUP BY 
  user_id;

We ran the same query seven times and measured the response times (red marking the longest time and green the shortest time):

Execution Time (seconds)
  Amazon Redshift Redshift Spectrum
CSV
Redshift Spectrum Parquet
Run #1 39.65 45.11 11.92
Run #2 15.26 43.13 12.05
Run #3 15.27 46.47 13.38
Run #4 21.22 51.02 12.74
Run #5 17.27 43.35 11.76
Run #6 16.67 44.23 13.67
Run #7 25.37 40.39 12.75
Average 21.53  44.82 12.61

For simple queries, Amazon Redshift performed better than Redshift Spectrum, as we thought, because the data is local to Amazon Redshift.

What was surprising was that using Parquet data format in Redshift Spectrum significantly beat ‘traditional’ Amazon Redshift performance. For our queries, using Parquet data format with Redshift Spectrum delivered an average 40% performance gain over traditional Amazon Redshift. Furthermore, Redshift Spectrum showed high consistency in execution time with a smaller difference between the slowest run and the fastest run.

Comparing the amount of data scanned when using CSV/GZIP and Parquet, the difference was also significant:

Data Scanned (GB)
CSV (Gzip) 135.49
Parquet 2.83

Because we pay only for the data scanned by Redshift Spectrum, the cost saving of using Parquet is evident and substantial.

Complex query

Next, we compared the same three configurations with a complex query.

Execution Time (seconds)
  Amazon Redshift Redshift Spectrum CSV Redshift Spectrum Parquet
Run #1 329.80 84.20 42.40
Run #2 167.60 65.30 35.10
Run #3 165.20 62.20 23.90
Run #4 273.90 74.90 55.90
Run #5 167.70 69.00 58.40
Average 220.84 71.12 43.14

This time, Redshift Spectrum using Parquet cut the average query time by 80% compared to traditional Amazon Redshift!

Bottom line: For complex queries, Redshift Spectrum provided a 67% performance gain over Amazon Redshift. Using the Parquet data format, Redshift Spectrum delivered an 80% performance improvement over Amazon Redshift. For us, this was substantial.

Optimizing the data structure for different workloads

Because the cost of S3 is relatively inexpensive and we pay only for the data scanned by each query, we believe that it makes sense to keep our data in different formats for different workloads and different analytics engines. It is important to note that we can have any number of tables pointing to the same data on S3. It all depends on how we partition the data and update the table partitions.

Data permutations

For example, we have a process that runs every minute and generates statistics for the last minute of data collected. With Amazon Redshift, this would be done by running the query on the table with something as follows:

SELECT 
  user, 
  COUNT(*) 
FROM 
  events_table 
WHERE 
  ts BETWEEN ‘2017-08-01 14:00:00’ AND ‘2017-08-01 14:00:59’ 
GROUP BY 
  user;

(Assuming ‘ts’ is your column storing the time stamp for each event.)

With Redshift Spectrum, we pay for the data scanned in each query. If the data is partitioned by the minute instead of the hour, a query looking at one minute would be 1/60th the cost. If we use a temporary table that points only to the data of the last minute, we save that unnecessary cost.

Creating Parquet data efficiently

On the average, we have 800 instances that process our traffic. Each instance sends events that are eventually loaded into Amazon Redshift. When we started three years ago, we would offload data from each server to S3 and then perform a periodic copy command from S3 to Amazon Redshift.

Recently, Amazon Kinesis Firehose added the capability to offload data directly to Amazon Redshift. While this is now a viable option, we kept the same collection process that worked flawlessly and efficiently for three years.

This changed, however, when we incorporated Redshift Spectrum. With Redshift Spectrum, we needed to find a way to:

  • Collect the event data from the instances.
  • Save the data in Parquet format.
  • Partition the data effectively.

To accomplish this, we save the data as CSV and then transform it to Parquet. The most effective method to generate the Parquet files is to:

  1. Send the data in one-minute intervals from the instances to Kinesis Firehose with an S3 temporary bucket as the destination.
  2. Aggregate hourly data and convert it to Parquet using AWS Lambda and AWS Glue.
  3. Add the Parquet data to S3 by updating the table partitions.

With this new process, we had to give more attention to validating the data before we sent it to Kinesis Firehose, because a single corrupted record in a partition fails queries on that partition.

Data validation

To store our click data in a table, we considered the following SQL create table command:

create external TABLE spectrum.blog_clicks (
    user_id varchar(50),
    campaign_id varchar(50),
    os varchar(50),
    ua varchar(255),
    ts bigint,
    billing float
)
partitioned by (date date, hour smallint)  
stored as parquet
location 's3://nuviad-temp/blog/clicks/';

The above statement defines a new external table (all Redshift Spectrum tables are external tables) with a few attributes. We stored ‘ts’ as a Unix time stamp and not as Timestamp, and billing data is stored as float and not decimal (more on that later). We also said that the data is partitioned by date and hour, and then stored as Parquet on S3.

First, we need to get the table definitions. This can be achieved by running the following query:

SELECT 
  * 
FROM 
  svv_external_columns 
WHERE 
  tablename = 'blog_clicks';

This query lists all the columns in the table with their respective definitions:

schemaname tablename columnname external_type columnnum part_key
spectrum blog_clicks user_id varchar(50) 1 0
spectrum blog_clicks campaign_id varchar(50) 2 0
spectrum blog_clicks os varchar(50) 3 0
spectrum blog_clicks ua varchar(255) 4 0
spectrum blog_clicks ts bigint 5 0
spectrum blog_clicks billing double 6 0
spectrum blog_clicks date date 7 1
spectrum blog_clicks hour smallint 8 2

Now we can use this data to create a validation schema for our data:

const rtb_request_schema = {
    "name": "clicks",
    "items": {
        "user_id": {
            "type": "string",
            "max_length": 100
        },
        "campaign_id": {
            "type": "string",
            "max_length": 50
        },
        "os": {
            "type": "string",
            "max_length": 50            
        },
        "ua": {
            "type": "string",
            "max_length": 255            
        },
        "ts": {
            "type": "integer",
            "min_value": 0,
            "max_value": 9999999999999
        },
        "billing": {
            "type": "float",
            "min_value": 0,
            "max_value": 9999999999999
        }
    }
};

Next, we create a function that uses this schema to validate data:

function valueIsValid(value, item_schema) {
    if (schema.type == 'string') {
        return (typeof value == 'string' && value.length <= schema.max_length);
    }
    else if (schema.type == 'integer') {
        return (typeof value == 'number' && value >= schema.min_value && value <= schema.max_value);
    }
    else if (schema.type == 'float' || schema.type == 'double') {
        return (typeof value == 'number' && value >= schema.min_value && value <= schema.max_value);
    }
    else if (schema.type == 'boolean') {
        return typeof value == 'boolean';
    }
    else if (schema.type == 'timestamp') {
        return (new Date(value)).getTime() > 0;
    }
    else {
        return true;
    }
}

Near real-time data loading with Kinesis Firehose

On Kinesis Firehose, we created a new delivery stream to handle the events as follows:

Delivery stream name: events
Source: Direct PUT
S3 bucket: nuviad-events
S3 prefix: rtb/
IAM role: firehose_delivery_role_1
Data transformation: Disabled
Source record backup: Disabled
S3 buffer size (MB): 100
S3 buffer interval (sec): 60
S3 Compression: GZIP
S3 Encryption: No Encryption
Status: ACTIVE
Error logging: Enabled

This delivery stream aggregates event data every minute, or up to 100 MB, and writes the data to an S3 bucket as a CSV/GZIP compressed file. Next, after we have the data validated, we can safely send it to our Kinesis Firehose API:

if (validated) {
    let itemString = item.join('|')+'\n'; //Sending csv delimited by pipe and adding new line

    let params = {
        DeliveryStreamName: 'events',
        Record: {
            Data: itemString
        }
    };

    firehose.putRecord(params, function(err, data) {
        if (err) {
            console.error(err, err.stack);        
        }
        else {
            // Continue to your next step 
        }
    });
}

Now, we have a single CSV file representing one minute of event data stored in S3. The files are named automatically by Kinesis Firehose by adding a UTC time prefix in the format YYYY/MM/DD/HH before writing objects to S3. Because we use the date and hour as partitions, we need to change the file naming and location to fit our Redshift Spectrum schema.

Automating data distribution using AWS Lambda

We created a simple Lambda function triggered by an S3 put event that copies the file to a different location (or locations), while renaming it to fit our data structure and processing flow. As mentioned before, the files generated by Kinesis Firehose are structured in a pre-defined hierarchy, such as:

S3://your-bucket/your-prefix/2017/08/01/20/events-4-2017-08-01-20-06-06-536f5c40-6893-4ee4-907d-81e4d3b09455.gz

All we need to do is parse the object name and restructure it as we see fit. In our case, we did the following (the event is an object received in the Lambda function with all the data about the object written to S3):

/*
	object key structure in the event object:
your-prefix/2017/08/01/20/event-4-2017-08-01-20-06-06-536f5c40-6893-4ee4-907d-81e4d3b09455.gz
	*/

let key_parts = event.Records[0].s3.object.key.split('/'); 

let event_type = key_parts[0];
let date = key_parts[1] + '-' + key_parts[2] + '-' + key_parts[3];
let hour = key_parts[4];
if (hour.indexOf('0') == 0) {
 		hour = parseInt(hour, 10) + '';
}
    
let parts1 = key_parts[5].split('-');
let minute = parts1[7];
if (minute.indexOf('0') == 0) {
        minute = parseInt(minute, 10) + '';
}

Now, we can redistribute the file to the two destinations we need—one for the minute processing task and the other for hourly aggregation:

    copyObjectToHourlyFolder(event, date, hour, minute)
        .then(copyObjectToMinuteFolder.bind(null, event, date, hour, minute))
        .then(addPartitionToSpectrum.bind(null, event, date, hour, minute))
        .then(deleteOldMinuteObjects.bind(null, event))
        .then(deleteStreamObject.bind(null, event))        
        .then(result => {
            callback(null, { message: 'done' });            
        })
        .catch(err => {
            console.error(err);
            callback(null, { message: err });            
        }); 

Kinesis Firehose stores the data in a temporary folder. We copy the object to another folder that holds the data for the last processed minute. This folder is connected to a small Redshift Spectrum table where the data is being processed without needing to scan a much larger dataset. We also copy the data to a folder that holds the data for the entire hour, to be later aggregated and converted to Parquet.

Because we partition the data by date and hour, we created a new partition on the Redshift Spectrum table if the processed minute is the first minute in the hour (that is, minute 0). We ran the following:

ALTER TABLE 
  spectrum.events 
ADD partition
  (date='2017-08-01', hour=0) 
  LOCATION 's3://nuviad-temp/events/2017-08-01/0/';

After the data is processed and added to the table, we delete the processed data from the temporary Kinesis Firehose storage and from the minute storage folder.

Migrating CSV to Parquet using AWS Glue and Amazon EMR

The simplest way we found to run an hourly job converting our CSV data to Parquet is using Lambda and AWS Glue (and thanks to the awesome AWS Big Data team for their help with this).

Creating AWS Glue jobs

What this simple AWS Glue script does:

  • Gets parameters for the job, date, and hour to be processed
  • Creates a Spark EMR context allowing us to run Spark code
  • Reads CSV data into a DataFrame
  • Writes the data as Parquet to the destination S3 bucket
  • Adds or modifies the Redshift Spectrum / Amazon Athena table partition for the table
import sys
import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
import boto3

## @params: [JOB_NAME]
args = getResolvedOptions(sys.argv, ['JOB_NAME','day_partition_key', 'hour_partition_key', 'day_partition_value', 'hour_partition_value' ])

#day_partition_key = "partition_0"
#hour_partition_key = "partition_1"
#day_partition_value = "2017-08-01"
#hour_partition_value = "0"

day_partition_key = args['day_partition_key']
hour_partition_key = args['hour_partition_key']
day_partition_value = args['day_partition_value']
hour_partition_value = args['hour_partition_value']

print("Running for " + day_partition_value + "/" + hour_partition_value)

sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)

df = spark.read.option("delimiter","|").csv("s3://nuviad-temp/events/"+day_partition_value+"/"+hour_partition_value)
df.registerTempTable("data")

df1 = spark.sql("select _c0 as user_id, _c1 as campaign_id, _c2 as os, _c3 as ua, cast(_c4 as bigint) as ts, cast(_c5 as double) as billing from data")

df1.repartition(1).write.mode("overwrite").parquet("s3://nuviad-temp/parquet/"+day_partition_value+"/hour="+hour_partition_value)

client = boto3.client('athena', region_name='us-east-1')

response = client.start_query_execution(
    QueryString='alter table parquet_events add if not exists partition(' + day_partition_key + '=\'' + day_partition_value + '\',' + hour_partition_key + '=' + hour_partition_value + ')  location \'s3://nuviad-temp/parquet/' + day_partition_value + '/hour=' + hour_partition_value + '\'' ,
    QueryExecutionContext={
        'Database': 'spectrumdb'
    },
    ResultConfiguration={
        'OutputLocation': 's3://nuviad-temp/convertresults'
    }
)

response = client.start_query_execution(
    QueryString='alter table parquet_events partition(' + day_partition_key + '=\'' + day_partition_value + '\',' + hour_partition_key + '=' + hour_partition_value + ') set location \'s3://nuviad-temp/parquet/' + day_partition_value + '/hour=' + hour_partition_value + '\'' ,
    QueryExecutionContext={
        'Database': 'spectrumdb'
    },
    ResultConfiguration={
        'OutputLocation': 's3://nuviad-temp/convertresults'
    }
)

job.commit()

Note: Because Redshift Spectrum and Athena both use the AWS Glue Data Catalog, we could use the Athena client to add the partition to the table.

Here are a few words about float, decimal, and double. Using decimal proved to be more challenging than we expected, as it seems that Redshift Spectrum and Spark use them differently. Whenever we used decimal in Redshift Spectrum and in Spark, we kept getting errors, such as:

S3 Query Exception (Fetch). Task failed due to an internal error. File 'https://s3-external-1.amazonaws.com/nuviad-temp/events/2017-08-01/hour=2/part-00017-48ae5b6b-906e-4875-8cde-bc36c0c6d0ca.c000.snappy.parquet has an incompatible Parquet schema for column 's3://nuviad-events/events.lat'. Column type: DECIMAL(18, 8), Parquet schema:\noptional float lat [i:4 d:1 r:0]\n (https://s3-external-1.amazonaws.com/nuviad-temp/events/2017-08-01/hour=2/part-00017-48ae5b6b-906e-4875-8cde-bc36c0c6d0ca.c000.snappy.parq

We had to experiment with a few floating-point formats until we found that the only combination that worked was to define the column as double in the Spark code and float in Spectrum. This is the reason you see billing defined as float in Spectrum and double in the Spark code.

Creating a Lambda function to trigger conversion

Next, we created a simple Lambda function to trigger the AWS Glue script hourly using a simple Python code:

import boto3
import json
from datetime import datetime, timedelta
 
client = boto3.client('glue')
 
def lambda_handler(event, context):
    last_hour_date_time = datetime.now() - timedelta(hours = 1)
    day_partition_value = last_hour_date_time.strftime("%Y-%m-%d") 
    hour_partition_value = last_hour_date_time.strftime("%-H") 
    response = client.start_job_run(
    JobName='convertEventsParquetHourly',
    Arguments={
         '--day_partition_key': 'date',
         '--hour_partition_key': 'hour',
         '--day_partition_value': day_partition_value,
         '--hour_partition_value': hour_partition_value
         }
    )

Using Amazon CloudWatch Events, we trigger this function hourly. This function triggers an AWS Glue job named ‘convertEventsParquetHourly’ and runs it for the previous hour, passing job names and values of the partitions to process to AWS Glue.

Redshift Spectrum and Node.js

Our development stack is based on Node.js, which is well-suited for high-speed, light servers that need to process a huge number of transactions. However, a few limitations of the Node.js environment required us to create workarounds and use other tools to complete the process.

Node.js and Parquet

The lack of Parquet modules for Node.js required us to implement an AWS Glue/Amazon EMR process to effectively migrate data from CSV to Parquet. We would rather save directly to Parquet, but we couldn’t find an effective way to do it.

One interesting project in the works is the development of a Parquet NPM by Marc Vertes called node-parquet (https://www.npmjs.com/package/node-parquet). It is not in a production state yet, but we think it would be well worth following the progress of this package.

Timestamp data type

According to the Parquet documentation, Timestamp data are stored in Parquet as 64-bit integers. However, JavaScript does not support 64-bit integers, because the native number type is a 64-bit double, giving only 53 bits of integer range.

The result is that you cannot store Timestamp correctly in Parquet using Node.js. The solution is to store Timestamp as string and cast the type to Timestamp in the query. Using this method, we did not witness any performance degradation whatsoever.

Lessons learned

You can benefit from our trial-and-error experience.

Lesson #1: Data validation is critical

As mentioned earlier, a single corrupt entry in a partition can fail queries running against this partition, especially when using Parquet, which is harder to edit than a simple CSV file. Make sure that you validate your data before scanning it with Redshift Spectrum.

Lesson #2: Structure and partition data effectively

One of the biggest benefits of using Redshift Spectrum (or Athena for that matter) is that you don’t need to keep nodes up and running all the time. You pay only for the queries you perform and only for the data scanned per query.

Keeping different permutations of your data for different queries makes a lot of sense in this case. For example, you can partition your data by date and hour to run time-based queries, and also have another set partitioned by user_id and date to run user-based queries. This results in faster and more efficient performance of your data warehouse.

Storing data in the right format

Use Parquet whenever you can. The benefits of Parquet are substantial. Faster performance, less data to scan, and much more efficient columnar format. However, it is not supported out-of-the-box by Kinesis Firehose, so you need to implement your own ETL. AWS Glue is a great option.

Creating small tables for frequent tasks

When we started using Redshift Spectrum, we saw our Amazon Redshift costs jump by hundreds of dollars per day. Then we realized that we were unnecessarily scanning a full day’s worth of data every minute. Take advantage of the ability to define multiple tables on the same S3 bucket or folder, and create temporary and small tables for frequent queries.

Lesson #3: Combine Athena and Redshift Spectrum for optimal performance

Moving to Redshift Spectrum also allowed us to take advantage of Athena as both use the AWS Glue Data Catalog. Run fast and simple queries using Athena while taking advantage of the advanced Amazon Redshift query engine for complex queries using Redshift Spectrum.

Redshift Spectrum excels when running complex queries. It can push many compute-intensive tasks, such as predicate filtering and aggregation, down to the Redshift Spectrum layer, so that queries use much less of your cluster’s processing capacity.

Lesson #4: Sort your Parquet data within the partition

We achieved another performance improvement by sorting data within the partition using sortWithinPartitions(sort_field). For example:

df.repartition(1).sortWithinPartitions("campaign_id")…

Conclusion

We were extremely pleased with using Amazon Redshift as our core data warehouse for over three years. But as our client base and volume of data grew substantially, we extended Amazon Redshift to take advantage of scalability, performance, and cost with Redshift Spectrum.

Redshift Spectrum lets us scale to virtually unlimited storage, scale compute transparently, and deliver super-fast results for our users. With Redshift Spectrum, we store data where we want at the cost we want, and have the data available for analytics when our users need it with the performance they expect.


About the Author

With 7 years of experience in the AdTech industry and 15 years in leading technology companies, Rafi Ton is the founder and CEO of NUVIAD. He enjoys exploring new technologies and putting them to use in cutting edge products and services, in the real world generating real money. Being an experienced entrepreneur, Rafi believes in practical-programming and fast adaptation of new technologies to achieve a significant market advantage.

 

 

How AWS Managed Microsoft AD Helps to Simplify the Deployment and Improve the Security of Active Directory–Integrated .NET Applications

Post Syndicated from Peter Pereira original https://aws.amazon.com/blogs/security/how-aws-managed-microsoft-ad-helps-to-simplify-the-deployment-and-improve-the-security-of-active-directory-integrated-net-applications/

Companies using .NET applications to access sensitive user information, such as employee salary, Social Security Number, and credit card information, need an easy and secure way to manage access for users and applications.

For example, let’s say that your company has a .NET payroll application. You want your Human Resources (HR) team to manage and update the payroll data for all the employees in your company. You also want your employees to be able to see their own payroll information in the application. To meet these requirements in a user-friendly and secure way, you want to manage access to the .NET application by using your existing Microsoft Active Directory identities. This enables you to provide users with single sign-on (SSO) access to the .NET application and to manage permissions using Active Directory groups. You also want the .NET application to authenticate itself to access the database, and to limit access to the data in the database based on the identity of the application user.

Microsoft Active Directory supports these requirements through group Managed Service Accounts (gMSAs) and Kerberos constrained delegation (KCD). AWS Directory Service for Microsoft Active Directory, also known as AWS Managed Microsoft AD, enables you to manage gMSAs and KCD through your administrative account, helping you to migrate and develop .NET applications that need these native Active Directory features.

In this blog post, I give an overview of how to use AWS Managed Microsoft AD to manage gMSAs and KCD and demonstrate how you can configure a gMSA and KCD in six steps for a .NET application:

  1. Create your AWS Managed Microsoft AD.
  2. Create your Amazon RDS for SQL Server database.
  3. Create a gMSA for your .NET application.
  4. Deploy your .NET application.
  5. Configure your .NET application to use the gMSA.
  6. Configure KCD for your .NET application.

Solution overview

The following diagram shows the components of a .NET application that uses Amazon RDS for SQL Server with a gMSA and KCD. The diagram also illustrates authentication and access and is numbered to show the six key steps required to use a gMSA and KCD. To deploy this solution, the AWS Managed Microsoft AD directory must be in the same Amazon Virtual Private Cloud (VPC) as RDS for SQL Server. For this example, my company name is Example Corp., and my directory uses the domain name, example.com.

Diagram showing the components of a .NET application that uses Amazon RDS for SQL Server with a gMSA and KCD

Deploy the solution

The following six steps (numbered to correlate with the preceding diagram) walk you through configuring and using a gMSA and KCD.

1. Create your AWS Managed Microsoft AD directory

Using the Directory Service console, create your AWS Managed Microsoft AD directory in your Amazon VPC. In my example, my domain name is example.com.

Image of creating an AWS Managed Microsoft AD directory in an Amazon VPC

2. Create your Amazon RDS for SQL Server database

Using the RDS console, create your Amazon RDS for SQL Server database instance in the same Amazon VPC where your directory is running, and enable Windows Authentication. To enable Windows Authentication, select your directory in the Microsoft SQL Server Windows Authentication section in the Configure Advanced Settings step of the database creation workflow (see the following screenshot).

In my example, I create my Amazon RDS for SQL Server db-example database, and enable Windows Authentication to allow my db-example database to authenticate against my example.com directory.

Screenshot of configuring advanced settings

3. Create a gMSA for your .NET application

Now that you have deployed your directory, database, and application, you can create a gMSA for your .NET application.

To perform the next steps, you must install the Active Directory administration tools on a Windows server that is joined to your AWS Managed Microsoft AD directory domain. If you do not have a Windows server joined to your directory domain, you can deploy a new Amazon EC2 for Microsoft Windows Server instance and join it to your directory domain.

To create a gMSA for your .NET application:

  1. Log on to the instance on which you installed the Active Directory administration tools by using a user that is a member of the Admins security group or the Managed Service Accounts Admins security group in your organizational unit (OU). For my example, I use the Admin user in the example OU.

Screenshot of logging on to the instance on which you installed the Active Directory administration tools

  1. Identify which .NET application servers (hosts) will run your .NET application. Create a new security group in your OU and add your .NET application servers as members of this new group. This allows a group of application servers to use a single gMSA, instead of creating one gMSA for each server. In my example, I create a group, App_server_grp, in my example OU. I also add Appserver1, which is my .NET application server computer name, as a member of this new group.

Screenshot of creating a new security group

  1. Create a gMSA in your directory by running Windows PowerShell from the Start menu. The basic syntax to create the gMSA at the Windows PowerShell command prompt follows.
    PS C:\Users\admin> New-ADServiceAccount -name [gMSAname] -DNSHostName [domainname] -PrincipalsAllowedToRetrieveManagedPassword [AppServersSecurityGroup] -TrustedForDelegation $truedn <Enter>

    In my example, the gMSAname is gMSAexample, the DNSHostName is example.com, and the PrincipalsAllowedToRetrieveManagedPassword is the recently created security group, App_server_grp.

    PS C:\Users\admin> New-ADServiceAccount -name gMSAexample -DNSHostName example.com -PrincipalsAllowedToRetrieveManagedPassword App_server_grp -TrustedForDelegation $truedn <Enter>

    To confirm you created the gMSA, you can run the Get-ADServiceAccount command from the PowerShell command prompt.

    PS C:\Users\admin> Get-ADServiceAccount gMSAexample <Enter>
    
    DistinguishedName : CN=gMSAexample,CN=Managed Service Accounts,DC=example,DC=com
    Enabled           : True
    Name              : gMSAexample
    ObjectClass       : msDS-GroupManagedServiceAccount
    ObjectGUID        : 24d8b68d-36d5-4dc3-b0a9-edbbb5dc8a5b
    SamAccountName    : gMSAexample$
    SID               : S-1-5-21-2100421304-991410377-951759617-1603
    UserPrincipalName :

    You also can confirm you created the gMSA by opening the Active Directory Users and Computers utility located in your Administrative Tools folder, expand the domain (example.com in my case), and expand the Managed Service Accounts folder.
    Screenshot of confirming the creation of the gMSA

4. Deploy your .NET application

Deploy your .NET application on IIS on Amazon EC2 for Windows Server instances. For this step, I assume you are the application’s expert and already know how to deploy it. Make sure that all of your instances are joined to your directory.

5. Configure your .NET application to use the gMSA

You can configure your .NET application to use the gMSA to enforce strong password security policy and ensure password rotation of your service account. This helps to improve the security and simplify the management of your .NET application. Configure your .NET application in two steps:

  1. Grant to gMSA the required permissions to run your .NET application in the respective application folders. This is a critical step because when you change the application pool identity account to use gMSA, downtime can occur if the gMSA does not have the application’s required permissions. Therefore, make sure you first test the configurations in your development and test environments.
  2. Configure your application pool identity on IIS to use the gMSA as the service account. When you configure a gMSA as the service account, you include the $ at the end of the gMSA name. You do not need to provide a password because AWS Managed Microsoft AD automatically creates and rotates the password. In my example, my service account is gMSAexample$, as shown in the following screenshot.

Screenshot of configuring application pool identity

You have completed all the steps to use gMSA to create and rotate your .NET application service account password! Now, you will configure KCD for your .NET application.

6. Configure KCD for your .NET application

You now are ready to allow your .NET application to have access to other services by using the user identity’s permissions instead of the application service account’s permissions. Note that KCD and gMSA are independent features, which means you do not have to create a gMSA to use KCD. For this example, I am using both features to show how you can use them together. To configure a regular service account such as a user or local built-in account, see the Kerberos constrained delegation with ASP.NET blog post on MSDN.

In my example, my goal is to delegate to the gMSAexample account the ability to enforce the user’s permissions to my db-example SQL Server database, instead of the gMSAexample account’s permissions. For this, I have to update the msDS-AllowedToDelegateTo gMSA attribute. The value for this attribute is the service principal name (SPN) of the service instance that you are targeting, which in this case is the db-example Amazon RDS for SQL Server database.

The SPN format for the msDS-AllowedToDelegateTo attribute is a combination of the service class, the Kerberos authentication endpoint, and the port number. The Amazon RDS for SQL Server Kerberos authentication endpoint format is [database_name].[domain_name]. The value for my msDS-AllowedToDelegateTo attribute is MSSQLSvc/db-example.example.com:1433, where MSSQLSvc and 1433 are the SQL Server Database service class and port number standards, respectively.

Follow these steps to perform the msDS-AllowedToDelegateTo gMSA attribute configuration:

  1. Log on to your Active Directory management instance with a user identity that is a member of the Kerberos Delegation Admins security group. In this case, I will use admin.
  2. Open the Active Directory Users and Groups utility located in your Administrative Tools folder, choose View, and then choose Advanced Features.
  3. Expand your domain name (example.com in this example), and then choose the Managed Service Accounts security group. Right-click the gMSA account for the application pool you want to enable for Kerberos delegation, choose Properties, and choose the Attribute Editor tab.
  4. Search for the msDS-AllowedToDelegateTo attribute on the Attribute Editor tab and choose Edit.
  5. Enter the MSSQLSvc/db-example.example.com:1433 value and choose Add.
    Screenshot of entering the value of the multi-valued string
  6. Choose OK and Apply, and your KCD configuration is complete.

Congratulations! At this point, your application is using a gMSA rather than an embedded static user identity and password, and the application is able to access SQL Server using the identity of the application user. The gMSA eliminates the need for you to rotate the application’s password manually, and it allows you to better scope permissions for the application. When you use KCD, you can enforce access to your database consistently based on user identities at the database level, which prevents improper access that might otherwise occur because of an application error.

Summary

In this blog post, I demonstrated how to simplify the deployment and improve the security of your .NET application by using a group Managed Service Account and Kerberos constrained delegation with your AWS Managed Microsoft AD directory. I also outlined the main steps to get your .NET environment up and running on a managed Active Directory and SQL Server infrastructure. This approach will make it easier for you to build new .NET applications in the AWS Cloud or migrate existing ones in a more secure way.

For additional information about using group Managed Service Accounts and Kerberos constrained delegation with your AWS Managed Microsoft AD directory, see the AWS Directory Service documentation.

To learn more about AWS Directory Service, see the AWS Directory Service home page. If you have questions about this post or its solution, start a new thread on the Directory Service forum.

– Peter