This guide describes techniques for optimizing a deployment of the Jetty HTTP server and servlet container. While some of the techniques described here are particular to the Jetty server, many are generally applicable to any similar servlet server. Note that for a J2EE application server, often it is the web tier that controls the vast majority of requests entering the server. Thus optimization of the web tier is key to the optimization of the entire container.
Optimization is more of an art than a science, so this document does not present a specific solution. Instead the issues and parameters that need to be considered are discussed and "rules of thumb" are given where appropriate.
In order to optimize a servlet container it is important to understand how requests are delivered to the container and what resources are used to handle it.
Each user connecting to the webapp container will be using a browser or other HTTP client application. How that client connects to the server greatly effects the optimization process. Historically browsers would only send a single HTTP request over a TCP connection, which meant that each HTTP request incurred the latency and resource costs of establishing a connection to the server. In order to quickly render a page with many images, each requiring a request, browsers could open up to 8 connections to the server so that multiple requests could be outstanding at once. In some specific circumstances with HTTP/1.0 browsers multiple requests could be sent over a single connection.
Modern browsers are now mostly using HTTP/1.1 persistent connections that allow multiple requests per connection in almost all circumstances. Thus browsers now typically open only 1 or 2 connections to each server and send many requests over those connections. Browsers are increasingly using request pipelining so that multiple requests may be outstanding on a single connection, thus decreasing request latency and reducing the need for multiple connections.
This situation results in a near linear relationship between the number of server connections and the number of simultaneous users off the server:
SimultaneousUser * NconnectionPerClient == SimultaneousConnections
For Jetty and almost all java HTTP servers, each connection accepted by the server is allocated a thread to listen for requests and to handle those requests. While non-blocking solutions are available to avoid this allocation of a thread per connection, the blocking nature of the servlet API prevents these being efficiently used with a servlet container.
SimultaneousConnections <= Threads
Persistent connections are supported by the HTTP/1.1 protocol and to a lesser extent by the HTTP/1.0 protocol. The duration of these connections and how they interact with a webapp can greatly effect the optimization of the server and webapp.
A typical webapp will be comprised of a dynamically generated page with many static components such as style sheets and/or images. Thus to display a page a cluster of requests are sent for the main page and for the resources that it uses. It is highly desirable for persistent connections to be held at least long enough for all the requests of a single page view to be completed.
After a page is served to a user, there is typically a delay while the user reads or interacts with the page. After which another request cluster is sent in order to obtain the next page of the webapp. The delay between request clusters can be anything from seconds to minutes. It is desirable for the persistent connections to be held longer than this delay in order to improve the responsiveness of the webapp and to reduce the costs of new connections. However the cost of this may be many idle connections on the server which are consuming resources for no server throughput.
The duration that persistent connections are held is under the control of both the client and the server, either of which can close a connection at any time. The browsers cache settings may also greatly effect the use of persistent connections, as many requests for resources on a page may not be issued or may be handled with a simple 304-NotModified response.
There are several key objectives when optimizing a webapp container, unfortunately not all of them are compatible and you are often faced with a trade off between two or more objectives.
Throughput is the primary measure used to rate the performance of a web container and it is mostly measured in requests per second. Your efforts in optimizing the container will mainly be aimed at maximizing the request rate or at least ensuring a minimal rate is achievable. However you must remember that request rate is an imperfect measure as not all requests are the same and that it is simple to measure a request rate for load that is unlike a real load. Specifically:
Latency is a delay in the processing of requests and it is desirable to reduce latency so that web applications appear responsive to the users. There are two key sources of latency to consider:
While latency is not directly related to throughput, there is often a trade off to be made between reducing latency and increasing throughput. Server resources that are allocated to idle connections may be better deployed handing actual requests.
The processing of each request consumes server resources in the form of memory, CPU and time. Memory is used for buffers, program stack space and application objects. Keeping memory usage within a servers physical available memory is important for maximum throughput. Conversely using a servers virtual memory may allow increased simultaneous users and can also decrease latency.
Servers will have 1 or more CPUs available to process requests. It is important that the scheduling of these processors is done in such a way that they spend more time handling requests and less time organizing and switching between tasks.
The servers often allocate resources based on time and it is important to tune timeouts so that those resources have a high probability of being productively used.
Much of optimization is focused on providing maximum throughput under average or high offered load rates. However for many systems that wish to offer high availability and high quality of service, it is important to optimize the performance under extreme offered load, either to continue providing reasonable service to some of the offered load or to gracefully degrade service to all of the offered load.
 This can be estimated or measured from an actual live server. The type of information that is useful gather includes:
| Attribute | Variations | Comment |
|---|---|---|
| Request rate | average, peak | The number of requests per second |
| Connection rate | average, peak | The number of new connections established with the server per second |
| Simultaneous Users | average, peak | The number of users that a simultaneously interacting with the server. |
| Requests per page | average | The number of requests that is required to render a page of the webapp. Includes images and style sheets, but may be affected by client caching. |
| Page view time | average | The period of time that a typical user will view a page before requesting another from the webapp. |
| Session duration | average | The period of time that an average user will remain in contact with the server. This can be used to estimate session and memory requirements |
The most accurate way to measure the attributes listed above is to measure them on a live server that is handling real traffic for the webapp that you are trying to optimize. Statistics and log analysis can then be used to derive the information above.
Jetty supports statistics collection at both the server and context level. The following configuration excerpt shows how to turn on statistics for the server and for a particular web application:
<Configure class="org.mortbay.jetty.Server"> ... <Call name="addWebApplication"> <Arg>/myapp</Arg> <Arg>./webapps/myapp</Arg> <Set name="statsOn">false</Set> </Call> ... <Set name="statsOn">false</Set> ... </Configure>
While statistics can be enabled as above, it is probably just as convenient to turn them on using a JMX agent to the Jetty MBeans. If Jetty is run with JBoss or within a JMX server, then a JMX agent can be used to configure and view statistics collection
The following statistics attributes are available on the org.mortbay.http.HttpServer class or via the associated MBean which is normally named like "org.mortbay:Jetty=0":
| Attribute | Comment |
|---|---|
| statsOn | True if statistics collection is turned on. |
| statsOnMs | Time in milliseconds stats have been collected for |
| statsReset() | Reset statistics |
| connections | Number of connections accepted by the server since statsReset() called |
| connectionsOpen | Number of connections currently open that were opened since statsReset() called |
| connectionsOpenMax | Maximum number of connections opened simultaneously since statsReset() called |
| connectionsDurationAve | Sliding average duration in milliseconds of open connections since statsReset() called |
| connectionsDurationMax | Maximum duration in milliseconds of an open connection since statsReset() called |
| connectionsRequestsAve | Sliding average number of requests per connection since statsReset() called |
| connectionsRequestsMax | Maximum number of requests per connection since statsReset() called |
| errors | Number of errors since statsReset() called. An error is a request that resulted in an exception being thrown by the handler |
| requests | Number of requests since statsReset() called |
| requestsActive | Number of requests currently active |
| requestsActiveMax | Maximum number of active requests since statsReset() called |
| requestsDurationAve | Average duration of request handling in milliseconds since statsReset() called |
| requestsDurationMax | Get maximum duration in milliseconds of request handling since statsReset() called. |
The following statistics attributes are available on the org.mortbay.http.HttpContext class or via the associated MBean which is normally named like "org.mortbay:Jetty=0,HttpContext=0,context=/myappp":
| Attribute | Comment |
|---|---|
| statsOn | True if statistics collection is turned on |
| statsOnMs | Time in Milliseconds that stats have been collected for |
| statsReset() | Reset statistics |
| requests | Number of requests since statsReset() called |
| requestsActive | Number of requests currently active |
| requestsActiveMax | Maximum number of active requests since statsReset() called |
| responses1xx | Number of responses with 1xx status (Informal) since statsReset() called |
| responses2xx | Number of responses with 2xx status (Success) since statsReset() called |
| responses3xx | Number of responses with 3xx status (Redirection) since statsReset() called |
| responses4xx | Number of responses with 4xx status (Client Error) since statsReset() called |
| responses5xx | Number of responses with 5xx status (Server Error) since statsReset() called |
It may not be possible to measure actual live traffic of a deployment to be optimized. In this case estimates must be made to obtain a traffic profile on which to base your optimization. The following work sheets give some examples of how this may be done:
| Attribute | Formula | Example | Comment |
|---|---|---|---|
| SimultaneousUsers | - | 1000 | Estimated from marketing or other sources. |
| UserSessionDuration | - | 180 seconds | Time a single user spends interactive with the webapp. Estimated from marketing, usage trials or other sources. |
| AvePageViewTime | - | 30 seconds | Time between page requests from a single user. Estimated from marketing or usage trials or other sources. |
| PagesPerUserSession | UserSessionDuration/PageViewTime | 6 | |
| RequestsPerPageNoCache | - | 12 | Calculated from inspection of HTML |
| RequestsPerPageCache | - | 3 | Calculated from inspection of HTML and usage trials. |
| RequestsPerUserSession | RequestsPerPageNoCache+ (RequestsPerPageCache* (PagesPerUserSession-1)) | 27 | |
| RequestsPerSecPerUser | RequestsPerUserSession/ UserSessionDuration | 0.15 | |
| RequestsPerSec | SimultaneousUsers* RequestsPerSecPerUser | 150 | |
| ConnectionsPerUser | - | 2.5 | Measured from usage trials with estimated browser mix. |
| AverageConnections | SimultaneousUsers* ConnectionsPerUser | 2500 | |
| ConnectionsPerSecond | ConnectionsPerUser* SimultaneousUsers/ UserSessionDuration | 13.88 | Assuming persistent connections that will span entire user session. If connections will not span session the multiply by PagesPerUserSession |
| PeakRequestsPerSecond | 2*ConnectionsPerSecond + (RequestsPerPageNoCache- RequestsPerPageCache) * SimultaneousUsers/ UserSessionDuration | 77.76 | Based on SimultaneousUsers doubling in UserSessionDuration. The formula represents double the normal requests rate, plus the additional load of the new users loading the initial page with no cache. |
This work sheet is only indicative of an estimate process that can be used, specially the method for determining the peak request rate. If possible , several estimation techniques should be used and the worse case numbers assumed.
When running a cluster of application servers, it is often desirable to be able to handle the max expected load in the advent of a node failure. Thus once the single node traffic has been estimated or measured, the traffic loads for failure modes can be calculated:
| Nodes in Cluster | Failed Nodes | Load |
|---|---|---|
| 2 | 1 | 200% |
| 3 | 1 | 150% |
| 3 | 2 | 300% |
| 4 | 1 | 133% |
| 4 | 2 | 200% |
Once the expected traffic profile has been analysed, a test client can be used to generate load on the server that reflects realistic load. It is important to make sure that the test client used is generating realistic load:
Jetty has a few features that have been deprecated or that are particularly resource hungry. Before starting optimizing the more conventional attributes it is worthwhile to make sure that these features are turned off or minimally configured.
The Jetty request log mechanism has the ability to buffer its output in memory before writing this to a file, which was intended to reduce synchronization load on the server. Unfortunately analysis of actual performance shows that the a server with buffering turned on has around 5% maximum throughput. Prior to Jetty release 4.2.9 log buffering was turned on by default. This should be turned off:
<Configure class="org.mortbay.jetty.Server"> ... <Set name="RequestLog"> <New class="org.mortbay.http.NCSARequestLog"> <Arg><SystemProperty name="jetty.home" default="."/>/logs/yyyy_mm_dd.request.log</Arg> <Set name="retainDays">90</Set> <Set name="append">true</Set> <Set name="extended">false</Set> <Set name="buffered">false</Set> <Set name="logTimeZone">GMT</Set> </New> </Set> ... </Configure>
The Jetty server supports statistic collection at the server and at the context level. While stats collection itself does not involve significant work load, it does require synchronization in order to correctly count some statistics. On a multi CPU machine, this extra synchronization could significantly affect the performance of the server, thus statistics should be turned off while optimizing the server. Note that this is somewhat counter productive, as the statistics are very useful for measuring the results of optimization. Thus the recommended use of the server statistics is to measure the profile of real load being handled by the server. This profile can then be used in generating test load, hopefully from a test client which itself can generate statistics which can be used to evaluate optimizations.
Jetty releases from release 4.0.0 to 4.2.9 contained the SocketChannelListener implementation of the HttpListener interface. This implementation used the features of the java 1.4 NIO library to use non-blocking sockets for idle connections. The intent was to avoid allocating a java thread to idle connections. Unfortunately, due to the nature of the servlet API, the sockets had to be returned to blocking mode before control was passed to a servlet. The resulting constant changing of the NIO select sets proved to consume significantly more system resources than was saved by reducing the required number of threads. The SocketChannelListener has been deprecated since 4.2.10 and should not be used for any release unless for experimental purposes.
The Jetty HTTP Listeners in versions prior to 4.1.1 had a parameter called maxReadTime, which was used to limit the time a request handler would wait for request content (e.g. on a form POST). This parameter, like maxIdleTime, was used to set the SO timeout value on the underlying connection socket. Unfortunately, if the maxReadTime value was different to the maxIdleTime value, then the SO timeout value was changed twice for every request. This proved to cause a significant reduction of throughput of the server, in the order of 10%. Thus for Jetty versions prior to 4.1.1 it is important to set the maxIdleTime and maxReadTime parameters to the same value:
<Configure class="org.mortbay.jetty.Server"> <Call name="addListener"> <Arg> <New class="org.mortbay.http.SocketListener"> <Set name="port">8080</Set <Set name="minThreads">25</Set> <Set name="maxThreads">255</Set> <Set name="maxIdleTimeMs">60000</Set> <Set name="maxReadTimeMs">60000</Set> </New> </Arg> </Call> ...
For Jetty versions 4.1.1 or later, maxReadTime should not be set as it is ignored and produces a warning.
Memory is a key resource that must be managed in any optimization of a web container. The procedure is to:
To tune memory usage, using a profiling tool like optimizeIt of jProbe can be very useful, however it can also be done simply by monitoring the memory allocated to the process by the operating system.
Running a webapp can consume memory for:
Before optimizing your memory, it is important to establish that your webapp does not have any memory leaks. This is to say that no memory allocated when processing requests that cannot be freed when the server returns to idle. This can be determined by running the application with a constant low to medium load and monitoring the memory usage. The memory allocated should increase to a level and then stabilize. If the memory continues to grow and/or a OutOfMemory exception is eventually thrown, then the application has an object/memory leak. Such an application will not be able to run long term and the leak should be fixed before optimizing or deploying the webapp.
Note that application data caches or poor garbage collection (GC) behaviour may appear as a memory leak. If possible disable application caches or configure them to small sized in order to test the applications underlying memory requirements. The JVM may be forced to perform a GC after a fixed number of requests by the requestsPerGC attribute of HttpServer. This can be set to a low value to avoid large fluctuations in memory usage during this measurement phase:
<Configure class="org.mortbay.jetty.Server"> ... <Set name="requestsPerGC">100</Set> ... </Configure>
JVMs allocate a fixed amount of stack space per thread created. The stack space is used for storing parameters and other objects associated with a method call. The more nested method calls that you application requires (deeper stacks), then more stack space is required. Typically the default stack settings for JVMs are rather generous and are allocated per thread, thus significant savings can be made by tuning this allocation.
For many JVMs, the stack space allocation is controlled with the -Xss option and the following command runs Jetty with 96kb allocated per stack:
java -Xss96k -jar start.jar
The simplest way to measure your stack requirements is to reduce the stack allocated until complex requests fail with StackOverflowException. You then need to increase your stack allocation with a good safety margin, the size of which will depend greatly on your application as some may have large variation in stack usage, specially those that use recursion.
An estimate of the static and dynamic heap space usage is needed to optimize the memory allocation. This is best done by measuring memory usage under realistic steady load at several load levels. The Jetty HttpListener should be configured to have a low minimal threads setting, so that idle threads do not effect the measurements.
The following table shows some results for a simple test for memory usage using the unix ps command to determine the resident memory set size:
| Active connections/threads | Process size in kb | kb per connection |
|---|---|---|
| 0 | 23076 | |
| 20 | 27540 | 224 |
| 40 | 29352 | 90 |
| 60 | 31868 | 125 |
| 100 | 33852 | 49 |
| 150 | 38264 | 88 |
Extrapolating from this table gives the following approximate formula for memory usagage for this webapp:
memoryRequired = 23Mb + threads * 200kb
Ideally this formula should be tested with direct measurement under all load levels.
This formula can now be used to calculate the memory requirements for your system and the JVM parameters should set to ensure that enough memory is available when the maximum number of threads are in use. For the above example, if a maximum of 500 threads are required (see below) and a 128k stack size is used, then 120MB of memory is required and the JVMs memory parameters should be configures as follows:
java -Xss128k -Xms120m -jar start.jar
Alternately, the memory formula can be used in reverse. If a known amount of physical or virtual memory is available and must not be exceeded, then the maximum number of threads can be determined.
Memory usage for a node in a cluster cannot be measured by looking at a single node. If distributed sessions or EJBs are being used, then memory used on one node may be replicated on all nodes. For example, with distributed HTTP sessions, each node must have capacity to store all the sessions for all the nodes in the cluster.
For this reason, it is often desirable to not have large homogenous clusters. Rather a cluster of clusters topology can reduce the memory and failure contingency load on each node.
Once you have determined your traffic profile and your memory profile, it is now possible to tune your server by adjusting the parameters of the thread pool. Each Jetty HttpListener has a pool of threads that is used to allocate threads to accepted connections. The following parameters can be used to configure the thread pool of each listener:
| Parameter | Comment |
|---|---|
| maxThreads | limit to the number of threads that can be allocated to connections for that HTTP listener. This will effectively limit the number of simultaneous users of the server as well as the maximum memory usage. |
| minThreads | The minimum number of unused threads to keep within the thread pool. A large number of unused threads will allows a server to respond to a sudden increase in load with little latency. More importantly, a HTTP listener is considered to be low on resources once it's pool cannot allocate minThreads unused threads without exceeding max threads. |
| maxIdleTimeMs | The maximum time in milliseconds that a thread can be allocated to a connection without a request being received. This limits the duration of idle persistent connections. |
| lowResourcePersistTimeMs | An alternative value for maxIdleTimeMs to be used when the listener is low on resources (see minThreads). |
| poolName | If multiple HTTP Listeners are used, those with the same pool name will share the same thread pool. This avoid one listener running low on threads while another has idle threads. |
The primary objective of the maxThread setting is to protect the server from excess resource utilization from high connection or request rates. Without a limit to the maximum threads, it would be possible for arbitrary high load to be accepted by the server which would eventually lead to one of the following failure modes:
There are two main approaches to setting maxThreads:
If with either of these approaches, the estimated, measured or required maximum load requires a maxThread value that exhaust the system memory, CPU, connections or other resources, then the machine is not sufficient for that webapp. In this case, additional server resources (memory, CPU, kernel configuration) is required or a clustering solution can be considered.
Once a server has reached it's maximum number of threads, then any new connections attempted are held by the operating system until either they time out, a thread becomes available to accept the connection or they are refused when the operating system queue becomes full.
The minThreads value is used to control how a server degrades under extreme load. Once there are less than minThreads available in the thread pool, then the lowResourcePersisteTimeMs parameter can be used to free up other idle threads.
If a good estimate or measure of average and maximum load are known, then the minThreads value can be set to half the difference between the average and maximum.
minThreads == (maxThreads - averageConnections) / 2
Thus if maxThreads is 3000 and averageConnections is 2500, then minThreads could be set at 250, so that low resource timeouts will be applied once the actual connections exceeds 2750.
Alternately, minThreads may be set to protect excess memory usage. If maxThreads requires more memory than is physically available, then minThreads can be set to free resources once physical memory is exceeded. Using the memory formula example from above and if 47Mb of physical memory is available on the system (when running the OS), then for maxThreads == 200:
minThreads == maxThreads - ( ( 47Mb - 23Mb ) / 200kb ) == 80
The idle time of a thread is used to limit the time that an persistent connection can be idle. Higher values are desirable to reduce latency for a user and avoid the expense of recreating TCP/IP connections. However, if the value is set too high, it wil result in many connections being left open when the user is no longer browsing the webapp and the resources allocated to it are effectively wasted for a long period of time.
A good value to use for the maxIdleTimeMs is slightly longer than the average page view time for the application, so that persistent connections are held long enough to span the time between page requests for an average user.
A HTTP Listener is considered low on resources if there are less than minThreads available in the thread pool and a lowResourcePersistTimeMs can be set to replace maxIdleTimeMs so that idle connections can be freed for other connections. The reasoning for this is that once a server is low on resources, there is little benefit keeping resources allocated to idle connections in the hope that new requests will come from them.
With a low lowResourcesPersistTimeMs value set, performance will degrade more gracefully as maxThreads is approached.
The value of lowResourcePersistTimeMs should be long enough to ensure that all requests in the cluster for a page view can be served by a persistent connection. This is typcially governed by the network latency and should not be more than a few seconds and can be as low as a few hundred milliseconds for a good network.
If a server has multiple HTTP listeners configured, it may be desirable to share the thread pool between listeners, so that one listener is not starved or resources if the other has free threads. If you wish to reserve capacity for a particular listener, then a shared thread pool should not be used:
<Configure class="org.mortbay.jetty.Server"> ... <Call name="addListener"> <Arg> <New class="org.mortbay.http.SocketListener"> <Set name="port">8080</Set> <Set name="minThreads">80</Set> <Set name="maxThreads">200</Set> <Set name="maxIdleTimeMs">30000</Set> <Set name="lowResourcePersistTimeMs">2500</Set> <Set name="poolName">Listener</Set> </New> </Arg> </Call> <Call name="addListener"> <Arg> <New class="org.mortbay.http.SunJsseListener"> <Set name="port">443</Set> <Set name="poolName">Listener</Set> <Set name="keystore">./etc/demokeystore</Set> <Set name="password">OBF:1vny1zlo1x8e1vnw1vn61x8g1zlu1vn4</Set> <Set name="keyPassword">OBF:1u2u1wml1z7s1z7a1wnl1u2g</Set> </New> </Arg> </Call> ... </Configure>
Providing larger buffers for the HTTP Listeners allows more efficient processing and generation of content, with less blocking and content switching. It also allows the TCP/IP protocol to more efficiently run it's sliding window protocol and avoid network latencies. Prior to Jetty release 4.2.10, the default buffer size was 4096 bytes. This has now been increased to 8192 bytes. The buffer size can be set as follows:
<Configure class="org.mortbay.jetty.Server"> ... <Call name="addListener"> <Arg> <New class="org.mortbay.http.SocketListener"> <Set name="port">8080</Set> <Set name="minThreads">80</Set> <Set name="maxThreads">200</Set> <Set name="maxIdleTimeMs">30000</Set> <Set name="lowResourcePersistTimeMs">2500</Set> <Set name="poolName">Listener</Set> <Set name="bufferSize">8192</Set> </New> </Arg> </Call> ... </Configure>
Authenticated security constraints on a webapp can be expensive to check as often a realm is implemented using crypto algorithms or with a remote AAA server or database involved.
Frequently a webapp page is constructed with many images that are not sensitive and do not need to be protected with an authenticated security constraint. Significant performance gains can be obtained by excluding such static resources from a security constraint.
For example consider a webapp that protects the directory /private with an authenticated constraint, but has a number of non-sensitive images in the /private/images directory, then the following web.xml excerp can be used to protect the private directory without the expense of protecting the images directory.
... <security-constraint> <web-resource-collection> <web-resource-name>Authed User Required</web-resource-name> <url-pattern>/private/*</url-pattern> </web-resource-collection> <auth-constraint> <role-name>*</role-name> </auth-constraint> </security-constraint> <security-constraint> <web-resource-collection> <web-resource-name>Images Not Protected</web-resource-name> <url-pattern>/private/images/*</url-pattern> <http-method>GET</http-method> <http-method>HEAD</http-method> </web-resource-collection> </security-constraint>
Logging of requests can add extra CPU load per request and an extra synchronization point. The following points should be considered to optimize the logging configuration:
The following request log configuration applies the points above.
<Configure class="org.mortbay.jetty.Server"> ... <Set name="RequestLog"> <New class="org.mortbay.http.NCSARequestLog"> <Set name="filename">./logs/yyyy_mm_dd.request.log</Set> <Set name="buffered">false</Set> <Set name="retainDays">90</Set> <Set name="append">true</Set> <Set name="extended">false</Set> <Set name="logTimeZone">GMT</Set> <Set name="ignorePaths"> <Array type="String"> <Item>/images/*</Item> <Item>*.css</Item> </Array> </Set> </New> </Set> ... </Configure>
The way a web application is written can greatly effect the efficiency of the service. The following points should be considered when writing or reviewing your webapplication:


Edition of the application with id 1
| Name | Description | Actions |
|---|---|---|
| Name | Description | Actions |
| Resource | Action | Description | Actions |
|---|---|---|---|
| Sample resource | Sample action | Sample description | Sample action |
| Name | Application | Actions |
|---|---|---|
| Sample name | Sample application | Sample actions |


{# TODO: this template is not jinja2 compatible and also it's ugly #}
{% for choice in field.choice_tuples %}
{# TODO: rendering field.input.attrs here seems bad.. we already render some of them manually so won't this create duplicates? #}
{% if not forloop.last %} {% endif %}
{% endfor %}


Parker Cammans is future coder who is excited to embark on his new journey. Although Parker does not have much direct experience with coding, he has been given several chances to prove he is capable. Parker spent several years as a professional mountain and road bike mechanic. This is where parker developed a deep love for problem solving and developed a keen eye for fine details. Parker’s passions then lead him to further opportunities when he was able to, work for ENVE composites as a test lab technician. Working with such unique machines sparked Parker’s creativity which lead him to MIT’s professional Certificate in coding.


imap_delete
(PHP 4, PHP 5, PHP 7)
imap_delete — Mark a message for deletion from current mailbox
说明
bool imap_delete ( resource $imap_stream , int $msg_number [, int $options = 0
] )
Marks messages listed in msg_number for deletion. Messages marked for deletion
will stay in the mailbox until either imap_expunge() is called or imap_close()
is called with the optional parameter CL_EXPUNGE.
参数
imap_stream
由 imap_open() 返回的 IMAP 流。
msg_number
The message number
options
You can set the FT_UID which tells the function to treat the msg_number
argument as an UID.
返回值
Returns TRUE.
范例
Example #1 imap_delete() example
Nmsgs . "
\n";
imap_delete($mbox, 1);
$check = imap_mailboxmsginfo($mbox);
echo "Messages after delete: " . $check->Nmsgs . "
\n";
imap_expunge($mbox);
$check = imap_mailboxmsginfo($mbox);
echo "Messages after expunge: " . $check->Nmsgs . "
\n";
imap_close($mbox);
?>
注释
Note:
IMAP mailboxes may not have their message flags saved between connections,
so imap_expunge() should be called during the same connection in order to
guarantee that messages marked for deletion will actually be purged.
参见
• imap_undelete() - Unmark the message which is marked deleted
• imap_expunge() - Delete all messages marked for deletion
• imap_close() - Close an IMAP stream
User Contributed Notes 10 notes
===============================================================================
I had some major issues deleting emails using this function. Using IIS 5.0 and
a win based Mail Server, I could not delete the emails individually.
My script merely needed to check the emails and update the database for bounce
backs, after which I simply wanted to erase all emails.
If imap_delete($mbox,$email->MsgNo) just isnt working for you, you can try
using
imap_delete($mbox,'1:*');
to clear out an entire mailbox.
Hope this helps cause it drove me insane for about 5 hours. :)
===============================================================================
I couldn't get imap_delete to work when I used it with pop3. Instead i wrote:
===============================================================================
the function is using the message number (and not the uid !)
$uid = $mail->uid;
$msgno = $mail->msgno;
if you want to use the uid, simply option value
imap_delete($mbox, $uid, FT_UID);
===============================================================================
This function seems not to work on "[Gmail]/All Mail" mailbox. This is because
Gmails keeps only one copy of the message and mailboxes are no more than
labels.
Deleting a message from a mailbox only removes that label (except for "All
Mail" mailbox).
To delete from "All Mail" you have to use imap_mail_move() function and move
the message to "[Gmail]/Trash" mailbox.
Note: Names of mailboxes in Gmail depends on the language you're using. E.g.:
in Spanish "All Mail" is "Todos" and "Trash" is "Papelera".
===============================================================================
In case you feel the need to kill all emails from before a certain year on your
Gmail account, this would work:
Nmsgs; $n++) {
echo "processing {$n} of {$mbox->Nmsgs}...\r";
$hdr = imap_fetchheader($imap, $n);
preg_match('/^Date: (.*?)$/m', $hdr, $matches);
$date = date_parse(strtotime($matches[1]));
if($date['year'] < $year) {
imap_delete($imap, $msg->msgno);
$flaggedForDelete++;
}
}
echo "expunging mailbox ({$flaggedForDelete} messages flagged)... ";
echo ((imap_expunge($imap)) ? "ok" : "failed!")."\n";
imap_close($imap);
?>
===============================================================================
After a little experimentation i found out how to mark a single message for
deletion...
imap_delete() seems to want a range, so to select one, simply range from your
id to your id.
Hope this helps.
===============================================================================
Somehow, when ever I try to delete an email using the message number returned
by imap_header, any email with a message number below 999 will fail.
I discovered that the imap_header will pad the message number with spaces. You
will need to trim the number before calling imap_delete.
I am using qmail and it took me over a month to figure it out.
Hope this will be helpful to some poor soul out there facing the same problem.
===============================================================================
// is not a complete code but enough to clear out an entire mailbox.
// hope this can save your time :-)
Nmsgs > 0) or die("Trash is empty already !");
// delete email(s)
@imap_delete($conn,'1:*'); // to clear out an entire mailbox.
@imap_expunge($conn);
echo "Trash is empty.";
imap_close($conn);
}
else
{
echo "


Suggested range is {{vendor.range.min.display}} to {{vendor.range.max.display}}. For quotes above this range, please contact {{vendor.contactPerson.name}} at
You can change the amount and click re-calculate quote.
{{buttonText}}Or, get a new quote
| {{option.name}} | {{item.term}} |
|---|---|
| {{quote.totalCostDisplay}} |
{{item.paymentDisplay}} Select |
Or, get a new quote


Gatekeeper Bella:
Here in Gludio there is an Airship Dock for Airships heading to Gracia.
Gracia is surrounded by a powerful magic force field, so it's impossible to enter it via teleportation or by sea. Only Airships made in Gracia are able to safely travel between the two continents.
A dock made from floating stones was constructed nearby; it took years of work, believe me. Boarding is conducted through the Airship Controller.
The only way to journey to the Gracia Continent is to use this dock. If you wish, I can teleport you there now.
Teleport to Gludio Airship Field.
Back


3 Balance sheet notes Debtors
Use this note to record information about people who owe money for goods or services your company provided as part of its normal trading activities
Use this note to record information about people who owe money for goods or services your company provided as part of its normal trading activities


--- layout: photo_post title: Underwater description: "Dec 2015: humans have explored only about 5% of the ocean!" group: "photo_navigation" path: photo_essays next_project: California 101 next_project_path: california_101 categories: [Underwater, Phtography, Analog, Beach, Black and White] images: - underwater_1.jpg - underwater_2.jpg - underwater_3.jpg - underwater_4.jpg - underwater_5.jpg - underwater_6.jpg - underwater_7.jpg - underwater_8.jpg - underwater_9.jpg - underwater_10.jpg ---
{{ page.description }}
Thanks Mahee, Georgina, Jojia!


{% extends "base.html" %} {% block title %}New Members{% endblock %} {% block content %}
There are no new members to approve - get recruiting!


Prop
Properties utilities for Crystal lang.
Mixin module that should be included in a class or a struct.
This module improves the std's accessor macros (getter
, getter!
, getter?
, property
, ...).
It provide a kind of IoC (Inversion Of Control).
Installation
- Add the dependency to your
shard.yml
:
dependencies:
prop:
github: nicolab/crystal-prop
- Run
shards install
Usage
📘 API doc.
If you are looking for a validator to validate data before instantiating a class or a struct,
you may be interested by validator.
This validator shard uses Prop
internally
to define and handle validation rules on each instance variable.
Development
Install dev dependencies:
shards install
Run:
crystal spec
Clean before commit:
crystal tool format
./bin/ameba
Contributing
- Fork it (https://github.com/Nicolab/crystal-prop/fork)
- Create your feature branch (
git checkout -b my-new-feature
) - Commit your changes (
git commit -am 'Add some feature'
) - Push to the branch (
git push origin my-new-feature
) - Create a new Pull Request
LICENSE
MIT (c) 2021, Nicolas Talle.
Author
| | |---| | Nicolas Talle | | |


§19. Nothing contained in this Act shall operate to confer United States nationality, nor to terminate nationality heretofore lawfully acquired, or restore nationality heretofore lost under any law of the United States or under any treaty to which the United States is or was a party.


--- layout: default title: Glyphdex permalink: /dex/ ---
Use this page to keep track of what glyphs you already have, currently in progress, will make it work more like wf.xuerian.net.
To "save" your list you need to copy / bookmark the url after you check all the glyphs you already have and just keep updating that.


iBoard.github
Home
links
readme
iBoard.cc
My Open Source
px.iboard.cc, the elix/phoenix implementation of iBoard
Digital Ocean host with Docker images deployed by Capistrano
Digital Ocean Profile
YARB – Simple Rails4 Starter Application using PStore
git.geekli.st
WAT Web Application Template
CBA Community Base Application
Sorted Array
jSort
– A gem to sort by drag & drop
Samples & Experiments
My ruby implementation of 'Conway's Game Of Life'
Sortable Name Class (ruby)
Ruby Shell Tools
– A Curses Experiment
Me on Github ...
Repox - Elixir Repository Pattern
My Gists
Contact & Collaboration
G+ Collaboration Page
Discussions (Google Group)
Clean Coders G+ Community
Programming Kitchen YouTube-channel
S3.index
px.iboard.cc
About ...
about.me ...
+Andreas Altendorfer
TheProgrammingKitchen
@nickendell
Feedly/nockenfell
Web
|
Blog
runtastic
|
Pinterest
Stack Overflow
License
All my public repositories on Github can be used under the terms of the MIT-license.
No warranty.
last updated: July 2016


Bills with Senate Actions on 06/21/2017
| Select a Date: |
Total Bill Actions: 123
| Bill | Short Title | Action | Date | Action Text |
|---|---|---|---|---|
| H21 | Driver Instruction/Law Enforcement Stops. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H26 | Workers' Comp/Approval of Disputed Legal Fees. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H27 | Clarify Expiration of Vehicle Registration. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H30 | Colorectal Cancer Awareness License Plate. | S | 06/21/2017 | Reptd Fav |
| S | 06/21/2017 | Re-ref Com On Finance | ||
| H56 | Amend Environmental Laws. | S | 06/21/2017 | Reptd Fav Com Substitute |
| S | 06/21/2017 | Com Substitute Adopted | ||
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H84 | DL/Deaf or Hard of Hearing Designation. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H89 | Housing Authority Transfers. | S | 06/21/2017 | Reptd Fav |
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H95 | Truck Deliveries to Port/Night Travel. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H98 | Crim. Offense/Vandalize Fire & EMS Equipment. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H120 | National Guard Can Purchase From CE. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H155 | Omnibus Education Law Changes. | S | 06/21/2017 | Reptd Fav Com Substitute |
| S | 06/21/2017 | Com Substitute Adopted | ||
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H161 | Divestment From Companies That Boycott Israel. | S | 06/21/2017 | Reptd Fav Com Substitute |
| S | 06/21/2017 | Com Substitute Adopted | ||
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H162 | Amend Administrative Procedure Laws. | S | 06/21/2017 | Reptd Fav Com Substitute |
| S | 06/21/2017 | Com Substitute Adopted | ||
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H224 | Warrant Check of Inmates in Custody. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H236 | NCAOC Omnibus Bill. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H243 | Strengthen Opioid Misuse Prevention (STOP)Act. | S | 06/21/2017 | Amend Adopted A1 |
| S | 06/21/2017 | Passed 2nd Reading | ||
| S | 06/21/2017 | Passed 3rd Reading | ||
| S | 06/21/2017 | Engrossed | ||
| H268 | City of Belmont Charter Revisions. | S | 06/21/2017 | Regular Message Sent To House |
| H272 | Fox and Coyote Trapping. | S | 06/21/2017 | Reptd Fav |
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H275 | No Stormwater Fees on Taxiways or Runways. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H294 | Unclaimed Property Notice Requirements.-AB | S | 06/21/2017 | Reptd Fav Com Substitute |
| S | 06/21/2017 | Com Substitute Adopted | ||
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H325 | Amend Arson Law. | S | 06/21/2017 | Reptd Fav Com Substitute |
| S | 06/21/2017 | Com Substitute Adopted | ||
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H343 | Enforcement of DVPO on Appeal. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H349 | Currituck-Developer Funds for Road Constr. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H353 | Authorize State Park System Expansion.-AB | S | 06/21/2017 | Reptd Fav Com Substitute |
| S | 06/21/2017 | Com Substitute Adopted | ||
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H374 | Business Freedom Act. | S | 06/21/2017 | Reptd Fav |
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H383 | NAIC Models/ORSA & Credit for Reinsurance-AB | S | 06/21/2017 | Reptd Fav Com Substitute |
| S | 06/21/2017 | Com Substitute Adopted | ||
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H393 | Mebane Charter/Alamance-Burl BOE Exchange. | S | 06/21/2017 | Reptd Fav |
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H399 | Stop Images Taken W/O Consent From Dissemin. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H415 | Hatteras Village Community Center District. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H420 | Rockingham Cty Tourism Development Authority. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H434 | Coins/Currency/Bullion Sales Tax Exemption. | S | 06/21/2017 | Reptd Fav |
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H436 | Local Government/Regulatory Fees. | S | 06/21/2017 | Withdrawn From Com |
| S | 06/21/2017 | Re-ref to Finance. If fav, re-ref to Rules and Operations of the Senate | ||
| H445 | Brunswick Fire Protection Fees. | S | 06/21/2017 | Reptd Fav |
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H447 | Lexington City Bd. of Ed./City Council. | S | 06/21/2017 | Reptd Fav Com Substitute |
| S | 06/21/2017 | Com Substitute Adopted | ||
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H462 | Banking Law Amendments. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H486 | National Guard Education Assistance Changes. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H504 | City of Lincolnton/Even-Yr Elect'n/Mayor Term. | S | 06/21/2017 | Reptd Fav Com Substitute |
| S | 06/21/2017 | Com Substitute Adopted | ||
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H511 | Game Nights/Nonprofit Fund-Raiser. | S | 06/21/2017 | Reptd Fav Com Substitute |
| S | 06/21/2017 | Com Substitute Adopted | ||
| S | 06/21/2017 | Re-ref Com On Finance | ||
| H548 | Equalize Treatment of Wastewater Products. | S | 06/21/2017 | Reptd Fav |
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H584 | Real Prop./Error Correction & Title Curative. | S | 06/21/2017 | Reptd Fav Com Substitute |
| S | 06/21/2017 | Com Substitute Adopted | ||
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H657 | Improve Adult Care Home Regulation. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H707 | Lien Agent/Notice of Cancellation. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H716 | CMVs/Use of Platoons. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| H772 | Amend NC Int'l Arbitration/Conciliation Act. | S | 06/21/2017 | Reptd Fav |
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H800 | Various Changes to Charter School Laws. | S | 06/21/2017 | Reptd Fav Com Substitute |
| S | 06/21/2017 | Com Substitute Adopted | ||
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| H880 | PUV for Beekeeping Property. | S | 06/21/2017 | Withdrawn From Com |
| S | 06/21/2017 | Re-ref to Finance. If fav, re-ref to Rules and Operations of the Senate | ||
| S220 | Motor Fuel Tax Exemption for Joint Agency. | S | 06/21/2017 | Reptd Fav Com Substitute |
| S | 06/21/2017 | Com Substitute Adopted | ||
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| S253 | Partisan Elections/Certain School Boards. | S | 06/21/2017 | Withdrawn From Cal |
| S | 06/21/2017 | Placed On Cal For 06/22/2017 | ||
| S257 | Appropriations Act of 2017. | S | 06/21/2017 | Conf Report Adopted 3rd |
| S315 | Make Various Changes Regarding Higher Ed. | S | 06/21/2017 | Concurred In H/Com Sub |
| S | 06/21/2017 | Ordered Enrolled | ||
| S326 | Clarify HUT & Improve Vehicle Titling Process. | S | 06/21/2017 | Ratified |
| S604 | Homemade Alcoholic Beverage Tasting Permit. | S | 06/21/2017 | Reptd Fav Com Substitute |
| S | 06/21/2017 | Com Substitute Adopted | ||
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| S622 | Business Corporation Act Revisions. | S | 06/21/2017 | Reptd Fav |
| S | 06/21/2017 | Re-ref Com On Rules and Operations of the Senate | ||
| S681 | Honor Ralph Hunt, Sr., Former Member. | S | 06/21/2017 | Passed 2nd Reading |
| S | 06/21/2017 | Passed 3rd Reading | ||
| S | 06/21/2017 | Special Message Sent To House |
Total Bills with Actions: 54


Current {{gerne_name}} movies in database
Title
Director
Actor 1
Actor 2
Year
Duration
Rent
Purchase
{% for item in movies %}
{{item.title}}
{{item.director}}
{{item.actor1}}
{{item.actor2}}
{{item.year}}
{{item.duration}} mins
{% endfor %}
Return to Main Page


Line data Source code
1 : /* 2 : * Copyright (c) 2015 - 2022, Intel Corporation 3 : * SPDX-License-Identifier: BSD-3-Clause 4 : */ 5 : 6 : #ifndef POWERBALANCER_HPP_INCLUDE 7 : #define POWERBALANCER_HPP_INCLUDE 8 : 9 : #include <memory> 10 : 11 : namespace geopm 12 : { 13 : /// @brief Stay within a power cap but redistribute power to 14 : /// optimize performance. An average per compute node power 15 : /// maximum is maintained, but individual nodes will be 16 : /// allowed above or below this average. 17 : class PowerBalancer 18 : { 19 : public: 20 : /// @brief Construct a IPowerBalancer object. 21 12 : PowerBalancer() = default; 22 : /// @brief Destroy a IPowerBalancer object. 23 12 : virtual ~PowerBalancer() = default; 24 : /// @brief Should be called at the start of application 25 : /// execution with the average power cap across 26 : /// compute nodes. Should be called at the end of 27 : /// the second phase of execution to note that the 28 : /// power savings made across all compute nodes has 29 : /// been evenly redistributed. 30 : /// @param power_cap The new maximum power limit equal to 31 : /// the current power limit plus the amount of 32 : /// power saved that is being redistributed. 33 : virtual void power_cap(double cap) = 0; 34 : /// @brief The current power cap which cannot be exceeded 35 : /// without breaking contract that the average 36 : /// power budget across all compute nodes is 37 : /// maintained. 38 : /// @return The current value of the power cap. 39 : virtual double power_cap(void) const = 0; 40 : /// @brief Returns the current power limit prescribed for 41 : /// this node. 42 : /// @return The current power limit in units of Watts. 43 : virtual double power_limit(void) const = 0; 44 : /// @brief Notify power balancer that a new limit has been 45 : /// set with the governor. 46 : /// @param limit limit that was set. 47 : virtual void power_limit_adjusted(double limit) = 0; 48 : /// @brief Update the object with a runtime measured under 49 : /// the current power limit and test if the current 50 : /// runtime sample is reliable such that a call 51 : /// runtime_sample() can be made. 52 : /// @param measured_runtime Most recent measurement of the 53 : /// execution time for an epoch on the node being 54 : /// managed under the current power limit. 55 : /// @return True if a stable measurement of expected 56 : /// runtime for an epoch can be made with the 57 : /// runtime_sample() method, and false otherwise. 58 : virtual bool is_runtime_stable(double measured_runtime) = 0; 59 : /// @brief Return the expected execution time of an application 60 : /// epoch under the current power limit. 61 : virtual double runtime_sample(void) const = 0; 62 : /// @brief Sample the measured runtimes under the current 63 : /// power cap in the first phase of execution. 64 : /// This measurement will be aggregated across all 65 : /// compute nodes to find the largest runtime 66 : /// measured. 67 : virtual void calculate_runtime_sample(void) = 0; 68 : /// @param Set the target runtime which is the largest 69 : /// epoch execution time measured by any compute 70 : /// node since the application began or the last 71 : /// global increase to the power budget. 72 : /// @param largest_runtime The largest expected runtime 73 : /// for one epoch across all compute nodes under 74 : /// the current power budget. 75 : virtual void target_runtime(double largest_runtime) = 0; 76 : /// @brief During the second phase of execution the power 77 : /// limit is decreased until the epoch runtime on 78 : /// the compute node under management has increased 79 : /// to the runtime of the slowest compute node. 80 : /// This method is used to update the object with a 81 : /// new measurement and also test if the current 82 : /// power limit meets the requirements. If the 83 : /// method returns false, then the value returned 84 : /// by power_limit() may have been updated. The 85 : /// new power limit should be enforced for the next 86 : /// epoch execution. 87 : /// @param measured_runtime Most recent measurement of the 88 : /// execution time for an epoch on the node being 89 : /// managed under the current power limit. 90 : /// @return True if the current power limit is reliably 91 : /// close to the target runtime and excess power 92 : /// should be sent up to the root to be 93 : /// redistributed, and false if more trials are 94 : /// required. 95 : virtual bool is_target_met(double measured_runtime) = 0; 96 : /// @brief Query the difference between the last power cap 97 : /// setting and the current power limit. If this 98 : /// method is called and it returns zero then the 99 : /// trial delta used to lower the power limit is 100 : /// reduced by a factor of two. 101 : /// @return The difference between the last power cap and 102 : /// the current power limit in units of Watts. 103 : virtual double power_slack(void) = 0; 104 : /// @brief Returns a unique_ptr to a concrete object 105 : /// constructed using the underlying implementation 106 : static std::unique_ptr<PowerBalancer> make_unique(double ctl_latency); 107 : /// @brief Returns a shared_ptr to a concrete object 108 : /// constructed using the underlying implementation 109 : static std::shared_ptr<PowerBalancer> make_shared(double ctl_latency); 110 : }; 111 : } 112 : 113 : #endif


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A website for suggesting names!
Submit a new name
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Installing the Motherboard!
The motherboard is the component that everything plugs in to. You've worked with it already, but now we'll put it in the case!
You will need the screwdriver for this part.
This process will require you to prep your case, and you will also need a steady hand for placing this inside the case.
Click any images on this page to open them in a separate tab/window for better viewing.
Steps:
- You will first need to put the standoffs in place. These are tiny screw-like pegs that come with your PC case (seen to the left). They have a hole in the top for screwing down the motherboard. You will want to eye the placement of screw holes in your motherboard (highlighted in red in the images) and line them up with the holes in your PC case (highlighted in red in the case image at the bottom). Each board will have its own placement of standoff screw holes. You need to eye it yourself before putting the standoffs in your case. To put them in, they will screw in by hand. No screwdriver is required for them.
- Once again, the standoffs must be where they need to be. Once you have put them in the case, gently place the motherboard in the case to ensure they line up with the screw holes.
- Once you have ensured the standoffs are in place, take your I/O shield (the metal sheet with holes cut in it for your different ports) and put it in the case, where the biggest remaining hole is available. This will need to be oriented the way the motherboard is place, or you won't be able to fit the motherboard inside. Make sure it is turned the correct way, then push it into place (refer to your manual for further instruction). This will require some force. You will feel it pop into place when it is ready. Be careful not to cut yourself on the potentially sharp metal.
- When the I/O shield is in place, you can put the motherboard inside. Tilt it with the left side going in first, lining up the ports with the holes in the I/O shield. Lie it flat and make sure you push the ports through the holes in the I/O shield so that they will be accessible and so that the screw holes will line up with the standoffs.
- Begin taking the screws that come with your case (refer to the manual for identification) and screwing them through the motherboard hole and into the standoffs. This will keep the motherboard from being damaged by the metal of your case.
- Once everything is secured, you should double check your screws, standoffs, and I/O shield orientation. This is an important step, and it will save you trouble if you check all of this now.
It's important to check the manuals of your motherboard and case, for identifying all items required. This guide cannot predict every configuration you may have.
If you need a better idea for what the I/O shield looks like, click here for an image. Remember: the ports must line up with the shield once it is in the case!
Once you are done, the PC should look near finished! You should have full access to the ports on the back, and the motherboard should not move if you push on it.
We're almost done! The next step is optional, but if you want to game, you will need a graphics card!


Use of Osler's Terms of Use and Privacy Policy Generator is subject to our Terms of Use. Keep in mind that it is provided 'as is', without any representation or warranty of any kind. Use of Osler's Terms of Use and Privacy Policy generator is at your own risk. If you have any questions about the agreement generated with this tool, please contact Sam Ip<EMAIL_ADDRESS>or another member of the Osler Technology Group.


MQ_GETSETATTR
Section: Linux Programmer's Manual (2)
Updated: 2012-07-13
Index
Return to Main Contents
NAME
mq_getsetattr - get/set message queue attributes
SYNOPSIS
#include <sys/types.h>
#include <mqueue.h>
int mq_getsetattr(mqd_t mqdes, struct mq_attr *newattr,
struct mq_attr *oldattr);
Note:
There is no glibc wrapper for this system call; see NOTES.
DESCRIPTION
Do not use this system call.
This is the low-level system call used to implement
mq_getattr(3)
and
mq_setattr(3).
For an explanation of how this system call operates,
see the description of
mq_setattr(3).
CONFORMING TO
This interface is nonstandard; avoid its use.
NOTES
Glibc does not provide a wrapper for this system call; call it using
syscall(2).
(Actually, never call it unless you are writing a C library!)
SEE ALSO
mq_getattr(3),
mq_overview(7)
Index
- NAME
-
- SYNOPSIS
-
- DESCRIPTION
-
- CONFORMING TO
-
- NOTES
-
- SEE ALSO
-
This document was created by
man2html,
using the manual pages.
Time: 02:54:49 GMT, September 18, 2014


Netflix Project is my own exercise project where I got the data from kaggle. There are 3 datasets that I will use in this project, that data are Netflix Dataset, IMDB Dataset, and Good Books Dataset. First, I want to analyse Netflix Dataset and use IMDB Dataset to add more information regarding the Netflix content (TV Shows and Movies), then I try to see if there are a content that comes from a book.
Project Intro/Objective
The purpose of this project is to analyse Netflix Dataset with help form addtition data, create data visualization, exploratory data analysis, and create a model recommendation for Netflix Content.
Project Library
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Plotly
- Collections
- WordCloud
- sklearn (scikit-learn)
Data Preparation
In this section, I want to know the information regarding the Netflix Dataset. First, I want to look at the head of Netflix Dataset.
Then I want to look at the Netflix Dataset information, where I can get information regarding how many data are there for each columns and the data type. After that, I search for null data, is there any null data for each columns.
Data Cleansing and Exploratory Data Analysis
In this section, I want to explore the Netfllix Dataset where I can visualize the data and if needed, I will do some data cleansing.
Comparison of the Number between TV Shows and Movies on Netflix
In this section, I want to see the comparison of the number between TV Shows and Movies on Netflix. First, I want to divide between TV Shows and Movies from Netflix Content, then I will try to analyse for each TV Shows and Movies.
From the code above, I can divide the content between TV Shows and Movies. Then, I try to use countplot to see the difference in the sum between TV Shows and Movies on Netflix.
As you can see from the countplot above, there are more Movies than TV Shows on Netflix.
Date of Release
In this section, I want to know the date of release for TV Shows and Movies, and try to analyse for is there a time where more content released or not.
TV Shows
First, I have to create a data frame that contain information about the date of release like year, month, and the number of TV Shows which released that time. I drop the na columns then extract the year and month information from netflix_shows.
Then, with groupby I create the data frame using the extracted year, month, and list of month for the rows label. Then I use value_counts() to find the number of TV Shows released for a certain date and I fill the empty columns with 0. The result is date_shows_df where we can see how many TV Shows were released for a certain date.
To help me analyse the date of release, I create a heatmap so I can see the date of release for certain date much easier.
Movies
Same with the TV Shows above, I have to create data frame that contains date of release for the movies. I will use netflix movies then extract the year and month.
Then, I create the data frame using the same method as TV Shows above.
Then I create a heatmap so I can see the date of release for certain date much easier.
If we compare these two heatmap, we can see that in the year 2020 that there are still many TV Shows update than Movies update, because maybe that Movies required much budget rather than TV Shows.
In Netflix TV Shows Content Update year 2020, we can see that there are comparetively much more content was released, it is because January is before the pandemic begin and December is after several months of ongoing pandemic and people much more adapt to this pandemic. This pattern not happened in Netflix Movies Content Update.
Rating Analysis
In this section, I want to know the TV Rating for each content then try to find the most TV rating for each content.
From these two countplot, the largest count of Movies and TV Shows are made with the 'TV-MA' rating. 'TV-MA' rating is a rating assigned by TV Parental Guidelines designed for mature audiences only.
Second largest count of Movies and TV Shows is the 'TV-14', a content that may be inappropriate for children younger than 14 years of age.
But for the third largest, we got different result between Movies and TV Shows. Where the third largest of Movies is 'R', a content where children under 17 years old require accompanying parent or adult guardian. And the third largest of TV Shows is 'TV-PG', a content that contain some material that parents or guardians may find inappropriate for younger children.
Preparing Data from Netflix and IMDB dataset
Merge the Dataset then Perform Data Cleansing
First, we read the csv file of IMDb ratings.csv then use only the weighted_average_vote column.
Then, we read the csv file of IMDb moies.csv then use only title, year, and genre columns.
We combine these two data frames to create a new data frame called ratings.
Finally, we drop the duplicates data from the subset of Title, Release Year, and IMDB Rating. We did the drop duplicates to avoid the duplicate information of Movies or TV Shows.
Using .info, I want to know how many entries are there and the data type.
After created the ratings (imdb dataset) data frame, I will merge it with the netflix_df (netflix dataset) to create a data frame that contains Netfilx content and their IMDb information, we called it joint_netflix_imdb.
But, we got some dirty data on joint_netflix_imdb, there are some duplicate columns and the column with same name but contains different information, we need to clean this dirty data.
To clean the data, first we need to drop the duplicate columns that contains same information, so we drop title and Release Year columns.
Then we change all the columns to lowercase and change rating to tv_rating.
Store the result to netflix_imdb_df.
Analysis of Content on Netflix
Top Rated Content on Netflix
Using px.sunburst, we create a pie chart for top rated content and contry origin.
We can see Breakout does not have country information because on our dataset (netflix_df) it does not have information about the country (NaN).
Countries with Highest Rated Content
First, we have to create a new data frame that contains information about the countries and their number of content.
Then using that data frame, we create a funnel plot. We can see that United States and India are the countries with most content on Netflix.
Year Wise Analysis
Using seaborn, we create a countplot. From that plot, we can see that 2018 is the year with most released contents.
Top 10 Movies Creating Countries
In this section, I want to know which country that has created most movies on Netflix. First, we extract the country information from netflix_movies.
After that, we must count the number of the country.
After that, we plot that data using seaborn barplot. From that plot we can see that United States has created more movies than any other countries.
Duration of Movies
In this section, I want to know the lenght for each movies and try to find the good amount of duration for a movies. We extract the duration from netflix_movies and try to clean the data so we get only the minutes of the movies, it makes easier to plot.
We try to plot it using KDE plot so we can see the distribution of the duration for a movies. From KDE plot above, we can see that a good amount of movies duration on Netflix are between 75 - 120 mins.
WordCloud for Movies Genres
I want to create a WordCloud for movies genres, WordCloud will makes easier to see all of the movies genre in one image. First, using collections module, we try to count the genre in netflix_movies.
After that, we plot the WordCloud using wordcloud module. From that WordCloud, we can see more genres only using one image. The bigger the count, the bigger the word.
Lollipop Plot for Movies Genres
I create a lollipop plot to see the count of the movies genres. Lollipop plot is easier to use when we use data from collection module, so our data is from collection module, we can use lollipop plot.
From the Lollipop Plot above, we can see that the top three genres that have the highest amount of content on Netflix is International Movies, Dramas, and Comedies.
Most TV Shows Creating Country
In this section, I want to know which country that has created most tv shows on Netflix. First, we extract the country information from netflix_shows.
After that, we count the number of the country.
After that, we plot that data using seaborn barplot. From that plot we can see that United States top tv shows creating country on Netflix.
TV Shows with Most Seasons
In this section, I want to know which TV Shows that has most seasons on Netflix. Because netflix_shows columns that conatins seasons information not in numeric, we have to convert this. First we create a dummy called features that contain title and duration. Then we put the netflix_shows data frame in this dummy. After that, we replace the string value to empty value.
Finally, we convert that string datatype to integer.
After got the seasons with integer datatype, we sort the value to get the top 20 TV Shows that has most seasons.
Finally, we create a barplot using seaborn. We've got Grey's Anatomy for TV Shows with the most seasons with 16 seasons, also there are 2 others TV Shows that at least has 15 seasons, that TV Shows are NCIS and Supernatural.
TV Shows Season Analysis
After we know the top 20 TV Shows with most seasons, I want to know the distribution off all season. So I create a new data frame.
After that, I create a pie chart. From that Pie Chart, we can see that 66.7% of the TV Shows on Netflix only has 1 season. And we can say that majority of the TV Shows on Netflix has 1 to 3 seasons.
WordCloud for TV Shows Genres
In this section, I want to create a WordCloud for tv shows genres. First, using collections module, we try to count the genre in netflix_movies.
After that, we plot the WordCloud using wordcloud module.
TV Shows in United States
In this section, I want to see the oldest and the latest TV Shows in United States. First, I need to create a oldest tv shows and latest tv shows variable.
Using go.Figure, I create a table for the oldest and latest tv shows in United States. From that table, we can see that the oldest TV Shows in United States is from 1946, the title is Pioneers of African_American Cinema.
Content in Indonesia
In this section, I want to analyse Content in Indonesia. I analyse using director approach, where I try to analyse the content and the its director.
From the figure above, we can see that content in Indonesia is divided by popular director and not so popular director. Riri Riza and Rocky Soraya is the most popular director which each has 5 films on their name. The majority of Indonesia director has 1 content on their name. Then we try to sort the content from the latest year, we see that content from Indonesia on Netflix are all the latest contents, with the oldest content from 2019.
Recommendation System
Using TfidfVectorizer
In this section, I use TF-IDF for recomendation system. The TF-IDF(Term Frequency-Inverse Document Frequency (TF-IDF) ) score is the frequency of a word occurring in a document, down-weighted by the number of documents in which it occurs. This is done to reduce the importance of words that occur frequently in plot overviews and therefore, their significance in computing the final similarity score.
First, I must import the TfidfVectorizer from scikit-learn (sklearn). Then, I setting the TfidfVectorizer, I set the stop_words as english because all of the data is in english language. Then I replace NAN with an emprty string, after that I fit and transform the netflix_df['description'], because we will use the description to find a film that has similar plot. Lastly, we check the shape of tfidf_matrix. We've got tfidf_matrix that contains 17095 words and 7787 movies title.
After fot the tfidf_matrix, I must cosine_sim and set the get_recommendation function. To set cosine_sim, I import linear_kernel from sklearn then compute the cosine similarity matrix. Then I create indices varieable where it will be use as title index.
For get_recommendation function, we use indices for index. Then get the pairwise similarity scores off all movies then sort it based on similarity scores. Because I want to the recommendation system show only 10 movies, I took only top 10 most similar movies, then get the index and return the movies name.
After create the recommendation system using TfidfVectorizer, we try to use it. From these two examples below, we see that our recommendation is not good enough, so we need to upgrade our model.
Content Based Filtering on Multiple Metrics
I try to add another factor beside the description, I will try to use these factors:
- Title
- Cast
- Director
- Listed in
- Plot
Cleaning Data
Before use the data frame to a new model, I have to clean the netflix_df. again because we add new factors. First, I want to fillna with an empty data.
Then, I make a new function called clean_data that will convert all the words to lowercase.
After all the data in lowercase, I try to identifying all the factors and store it in fillna variable.
Then, I applying fillna to the clean_data function.
After got all the lowercase words for fillna, I create the bag of words. To create a bag of words, I create a new function called create_soup then applying fillna to this create_soup function.
Using CountVectorizer
In this section, I use CountVectorizer for recomendation system. CountVectorizer is used to transform a given text into a vector on the basis of the frequency (count) of each word that occurs in the entire text. This is helpful when we have multiple such texts, and we wish to convert each word in each text into vectors (for using in further text analysis).
The reason why I use CountVectorizer eventhough TfidfVectorizer works better that CountVectorizer is because we add more factors and I want to get the fast result because CountVectorizer only count the number of times a word appears in the document and TfidfVectorizer consider overall document weightage of a word.
First, I must import CountVecorizer and cosine_similarity from sklearn. Then use english as stop_words for CountVectorizer and fit_transform using fillna (bag of words), finally I compute the cosine similarity matrix.
For the index, I reset the index then create a new index for the movies.
For get_recommendation function, we use indices for index. Then get the pairwise similarity scores off all movies then sort it based on similarity scores. Because I want to the recommendation system show only 10 movies, I took only top 10 most similar movies, then get the index and return the movies name. This function work same as before.
After create the recommendation system using CountVectorizer, we try to use it. The difference with the TfidfVectorizer is on this get_recommendation, we also input the cosine_sim2, where cosine_sim2 is the new cosine similarity matrix.
Netflix Content from Books?
In this section, I want to know is there a content on Netflix that originates from books. First I load the books.csv data and called it books_df.
Then, I merge the books_df with netflix_df and called the new data frame as netflix_books. Before I merge the data frame, I convert the title on netflix_df and books_df to lowercase to make sure there is no error in merge the data because of the words case.
Finally, I create a Pie Chart from netflix_books using go.Pie. From the Pie chart below, we can see that the majority content on Netflix is not from a book, where Shows From Books is 3.58% and Shows Not From Books is 96.4%.
Additional Resources
- Header Backgrounds by Mollie Sivaram at unsplash.com
- Kaggle Original Challenge Source
- For further explanation regarding python code, please kindly check this link.


{% extends "base.html" %} {% block page_title %}CWR matching information{% endblock %} {% block content %}
Choose the sources for the matching.
External enrichment sources will be applied.
As this is a mock, these sources won't really be used.
Matching


This tool is for bid bot owners ONLY to be able to specify the settings for their bot in the "json_metadata" field of the bot account on the blockchain.
The bot tracker tool will pick up these settings, if they are available, so bot owners will no longer need to message me to update the tracker and will make the tracker more accurate since it will update instantly as changes are made.
The JSON format for these settings is:
"config": { "min_bid_sbd": 0.5, "bid_window": 2.4, "comments": false, "accepts_steem": true }
This tool was created to make it super easy for bot owners to set and update this information, but any tool can be used and the bot tracker will pick it up as long as the data is available in the correct format.
Information below is not saved / stored or sent anywhere. You can view the page source to verify.


This is the documentation for the FH-Potsdam ;-⟩ project week "Typography & Automation". The goal was to learn the basics of JavaScript for Adobe applications to apply this in automated layouts and generative art. Bring raw text into shape by rules. The project begun with an introduction to JavaScript and how it works with InDesign, then project and idea development to follow in individual and group meetings. At the end the contributors held a short presentation. For further info on how to write JavaScript for Adobe, have a look into the wiki, but first you should browse the projects below.
You can download the whole project as .zip or clone it with Git by running:
git clone git://github.com/fabiantheblind/auto-typo-adbe-id.git
For some of the results there is also a git submodule or gist included. They will point you to the original repo/gist where the code may be developed further.
#some clone urls for faster access
#swissd
git clone git://github.com/natael/swissd.git
#Glyphs-Scripts Python
git clone git://github.com/PDXIII/Glyphs-Scripts.git
#Font Fun Friday
git clone git://github.com/PDXIII/FontFunFriday.git
#Layout Invaders
git clone git://github.com/schlompf/layout-invaders.git
#myRPG
git clone git://gist.github.com/3874630.git gist-3874630
#CMYK colors
git clone git://gist.github.com/3877719.git
#Pagenumber Color Gradation
git clone git://gist.github.com/3877709.git
#Extract Highlight jQuery Widget
git clone git://gist.github.com/3923596.git gist-3923596
#The Wiki written in gollum https://github.com/github/gollum
git clone git://github.com/fabiantheblind/auto-typo-adbe-id.wiki.git
In a lot of these scripts the authors used specific fonts. So you have to change the fonts to make them work properly.
Look for the following code and change it to a font that exists on your system.
app.fonts.item
Also there could be some issues with pointSize if you use diffrent fonts. Keep an eye on that. If you run into any problems add an issue over here and we will get back to you with a fix.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.  IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


Differentiating Between Bounce Rates
Understanding your site’s bounce rate is essential to your online presence success.
A low bounce rate signifies ineffectiveness and lost revenue. A high bounce rate signifies your visitors are meeting your objectives and taking action.
Bounce rate is defined as percentage of visitors who come onto your site and leave your page, or site, after not clicking anywhere else on your site. It is a metric tool that measures the quality of your web pages.
You must understand that bounce rate and exit rate are not the same. Bounce rate is the percentage of people who visit any page of your site without going further. They click off within seconds, or even a split second. Exit rate, on the other hand, is when people leave your site after staying for more than a few seconds. Every visitor needs to exit your website, not every one needs to bounce off.
Before you can differentiate between different types of bounce rates, you must analyze different traffic sources. You need to determine where your traffic comes from and what their bounce rates are. If your incoming traffic sources have poor bounce rates, it will reflect poorly on your rate. The largest amount of converting traffic sources include search engine traffics, email marketing campaigns and affiliate campaigns.
Analyze incoming traffic
When analyzing your incoming traffic, forget about unqualified sources such as social media sites, unknown referrer sites and random directory sites.
Google Analytics is a wonderful tool for analyzing bounce rates. Combining the data obtained from Google Analytics with the spreadsheet capabilities of Excel 2013 will highlight which pages have increased bounce rates.
Increase in the bounce rate
Increased bounce rates occur for a variety of reasons including outdated and stale web content, ineffective ad-words and your competition changing tactics. Tactics include enhancing a marketing campaign, implementing effective ad-words, updating web content and reducing prices.
The effectiveness of your keywords, and phrases, will affect your bounce rate. Effective keywords, which provide visitors with information they are looking for, will result in lower bounce rates. On the other hand, if your website uses ineffective keywords, your visitor will leave as soon as they realize your site is not appropriate. Nothing is more frustrating in the Internet world than typing in a keyword or keyword phrase and being taken to a website that has nothing to do with what is being searched for.
Analyze keywords
Analyze the keywords on every page of your website with a website analyzer tool like Google Analytics. Make adjustments as needed.
Does your landing page contain action-oriented language that stimulates interest? Is the information specific and user-friendly? Is content organized optimally? Then, your bounce rate will be lower since visitors want to stay on your page. They will also be more inclined to view other pages on your site.
On the other hand, if your landing page is generalized and/or leads your visitor on a clicking search, your bounce rate will be high. Your landing page will not meet your visitor’s expectations and they will quickly leave.
ROI and Revenue
Bounce rates directly impacts your ROI and revenues. They can point out which information/pages need revisions, increase your conversion rate, highlight where you are wasting your money and help you focus in on your objectives. Are you using them to your advantage?


Black Christmas
Black Christmas is a movie starring Olivia Hussey, Keir Dullea, and Margot Kidder. During their Christmas break, a group of sorority girls are stalked by a stranger.


This date calculator offers four options. It enables you to calculate the number of days (from today) until a future date, the number of days between two dates, or add/subtract days from a selected date.
Select Calculator:
Select Date
|
Selected date: |
Selected date: |


In this guide, we’ll walk you through how to add a MacOS app to Bitrise, how to run Xcode tests, manage your code signing files and deploy the app to bitrise.io and to the App Store.
Adding a MacOS app ⚓
- Click the
+
sign on the top menu bar and selectAdd app
. - On the Create new App page, choose the account you wish to add the app to.
- Set the privacy of the app to either Private or Public and click
Next
. - Select the Git hosting service that hosts your repository, then find and select your repository that hosts the project. Read more about connecting your repository.
- When prompted to set up repository access, click
No, auto-add SSH key
. Read more about SSH keys. - Type the name of the branch that includes your project’s configuration - master, for example - then click
Next
. -
Wait while Bitrise is validating your project. We look for your configuration files and set up your app based on them.
IMPORTANT: the validation will fail if you do not have a SHARED scheme in your project. You can still point Bitrise manually to your Xcode scheme but if it’s shared, we automatically detect it for you. Read more about schemes and the possible issues with them!
-
Select the export method. You can modify this later - for now, select
development
.Once you clicked it, you should see your:
- Project or Workspace path
- Scheme name
- export method
- MacOS stack
- Register a webhook when prompted so that Bitrise can start a build automatically when code is pushed to your repository, or a pull request is created. This also kicks off your first build - click the message and it will take you to the build page.
Running Xcode tests ⚓
Once you created your app, the first build will run based on the automatically created primary workflow. You can check it out in the app’s Workflow Editor: click the app’s name on your Dashboard then click the Workflow
tab.
If you have test targets defined, the primary
workflow of a MacOS app includes the two Steps you need to run your Xcode tests, and view their results on bitrise.io:
Xcode Test for Mac
Deploy to Bitrise.io
The Xcode Test for Mac
step runs the pre-defined Xcode tests. It has a default configuration that does not need to be modified: if the tests are written correctly, they will work. You can find the same configuration options in Xcode, too.
The Deploy to Bitrise.io
will deploy the following to the Logs
and Apps & Artifacts tab of the build:
- your Xcode test results
- your raw
xcodebuildoutput
log.
Code signing and exporting a MacOS app ⚓
To install and test the app on other physical devices, you will need to create and export an .app or .pkg file. This requires setting up code signing. In the example, we’ll be exporting an app with the development
export method: you cannot upload such an app to Testflight but you can test it, for example, on the devices of your internal testers.
You will need:
- the automatically created
deploy
workflow - a Development certificate (a .p12 certificate file)
- a Development type Provisioning Profile. For a MacOS project, the file extension of the provisioning profile is .provisionprofile.
- Set the code signing type of your project in Xcode to either manual or automatic (Xcode managed), and generate the package file locally.
-
Collect and upload the code signing files with the codesigndoc tool.
The tool can also upload your code signing files to Bitrise - we recommend doing so! Otherwise, upload them manually: enter the Workflow Editor and select the
Code signing
tab, then upload the files in their respective fields. - Go to your app’s Workflow Editor, and select the
deploy
workflow in theWORKFLOW
dropdown menu in the top left corner. - Check that you have the
Certificate and profile installer
Step in your workflow. It must be before theXcode Archive for Mac
Step (you can have other Steps between the two, likeXcode Test for Mac
). -
Check the
Export method
input under theapp/pkg export configs
input group of theXcode Archive for Mac
Step.If you selected
development
when you added the app to Bitrise, you don’t need to change the input. Otherwise, manually set it todevelopment
. - Start a build.
If you uploaded the correct code signing files, the Certificate and profile installer
Step should install your code signing files and the Xcode Archive for Mac
Step should export an .app or .pkg file with the development export method. If you have the Deploy to Bitrise.io
Step in your workflow, you can find the binary package file on the Apps & Artifacts
tab of the build page.
Deploying to the App Store ⚓
If you set up your code signing files and created an .app or .pkg file for your internal testers, it is time to involve external testers and then to publish your MacOS app to the App Store. Let’s see how!
To deploy to the App Store, you will need more code signing files:
- a Mac App Distribution Certificate
- a Mac Installer Distribution certificate
- On your local machine, set up App Store code signing for your project in Xcode, and export an .app or .pkg file. If this fails locally, it will definitely fail on Bitrise, too!
- Collect and upload the code signing files with the codesigndoc tool.
- Go to the app’s Workflow Editor and create a new workflow: click the
+ Workflow
button, enter the name of your new workflow and in the BASED ON dropdown menu, selectdeploy
. This way the new workflow will be a copy of the basicdeploy
workflow. -
Set the
Export Method
input of theXcode Archive for Mac
Step toapp-store
.You can export multiple binaries with different export methods: use the
Export macOS Xcode archive
Step in your workflow. - Add the
Deploy to iTunes Connect - Application Loader
Step to your workflow, after theXcode Archive for Mac
Step but preferably before theDeploy to Bitrise.io
Step. -
Provide your Apple credentials in the
Deploy to iTunes Connect - Application Loader
Step.The Step will need your:
- Apple ID
- password or, if you use two-factor authentication on iTunes Connect, your application password.
Don’t worry, the password will not be visible in the logs or exposed - that’s why it is marked SENSITIVE.
And that’s it! Start a build - if everything went well, you should see your app on Testflight. From there, you can distribute it to external testers or release it to the App Store.


The Modelica Standard Library consists of the following main sub-libraries:
| Library Components | Description |
|---|---|
| Analog Analog electric and electronic components, such as resistor, capacitor, transformers, diodes, transistors, transmission lines, switches, sources, sensors. |
|
| Digital Digital electrical components based on the VHDL standard, like basic logic blocks with 9-value logic, delays, gates, sources, converters between 2-, 3-, 4-, and 9-valued logic. |
|
| Machines Electrical asynchronous-, synchronous-, and DC-machines (motors and generators) as well as 3-phase transformers. |
|
| FluxTubes Based on magnetic flux tubes concepts. Especially to model electro-magnetic actuators. Nonlinear shape, force, leakage, and material models. Material data for steel, electric sheet, pure iron, Cobalt iron, Nickel iron, NdFeB, Sm2Co17, and more. |
|
| Translational 1-dim. mechanical, translational systems, e.g., sliding mass, mass with stops, spring, damper. |
|
| Rotational 1-dim. mechanical, rotational systems, e.g., inertias, gears, planetary gears, convenient definition of speed/torque dependent friction (clutches, brakes, bearings, ..) |
|
| MultiBody 3-dim. mechanical systems consisting of joints, bodies, force and sensor elements. Joints can be driven by drive trains defined by 1-dim. mechanical system library (Rotational). Every component has a default animation. Components can be arbitrarily connected together. | |
| Fluid 1-dim. thermo-fluid flow in networks of vessels, pipes, fluid machines, valves and fittings. All media from the Modelica.Media library can be used (so incompressible or compressible, single or multiple substance, one or two phase medium). |
|
| Media Large media library providing models and functions to compute media properties, such as h = h(p,T), d = d(p,T), for the following media:
|
|
| FluidHeatFlow, HeatTransfer Simple thermo-fluid pipe flow, especially to model cooling of machines with air or water (pipes, pumps, valves, ambient, sensors, sources) and lumped heat transfer with heat capacitors, thermal conductors, convection, body radiation, sources and sensors. | |
| Blocks Input/output blocks to model block diagrams and logical networks, e.g., integrator, PI, PID, transfer function, linear state space system, sampler, unit delay, discrete transfer function, and/or blocks, timer, hysteresis, nonlinear and routing blocks, sources, tables. |
|
| StateGraph Hierarchical state machines with a similar modeling power as Statecharts. Modelica is used as synchronous action language, i.e., deterministic behavior is guaranteed |
|
A = [1,2,3; 3,4,5; 2,1,4]; b = {10,22,12}; x = Matrices.solve(A,b); Matrices.eigenValues(A); |
Math, Utilities Functions operating on vectors and matrices, such as for solving linear systems, eigen and singular values etc., and functions operating on strings, streams, files, e.g., to copy and remove a file or sort a vector of strings. |


Biocur1851.github.io
Technical Data Sheet imc CANSAS-L-HCI8
CANSAS-HCI8-T-L, imc CANSAS Module - A-Tech. The imc CANSAS-HCI8 measurement module allows safe and precise measurement of temperatures and low voltage signals riding on a high or indeterminate common mode level of up to 800 V. Typical applications include voltage or temperature measurements on high-voltage components for electric or hybrid vehicles, such as batteries, fuel cells and power circuits. The imc CANSAS-HCI8 measurement module allows safe and precise measurement of temperatures and low voltage signals riding on a high or indeterminate common mode level of up to 800 V. Typical applications include voltage or temperature measurements on high-voltage components for electric or hybrid vehicles, such as batteries, fuel cells. PDF Technical Data Sheet imc CANSAS-L-HCI8
The imc CANSAS-L-HCI8-2T module enables high-isolated temperature measuring over 8 channels. The measurement inputs are type-K thermocouple sockets for high voltage. The measurement inputs are type-K thermocouple sockets for high voltage. Imc CANSAS - classic - imc Test Measurement.
Imc CANSAS-L-HCI8-2T Supports imc Plug Measure TEDS (Transducer Electronic Data Sheets, IEEE 1451.4) Support of TEDS for storing and exporting sensor information. Parameterization of a measurement channel at the click of a mouse. Typical applications:.
The CANSAS-HCI8-T-L module allows safe and precise measurement with 8 thermocouples (Type K) which are mounted on high-voltage components with common mode voltage levels Imc CANSAS can be employed in a wide variety of settings. To meet the requirements of different measurements, there exists models of different heights and lengths, all with extruded aluminum housings and no internal. Imc CANSAS-L-HCI8-2T Supports imc Plug Measure TEDS (Transducer Electronic Data Sheets, IEEE 1451.4) Support of TEDS for storing and exporting sensor information. The imc CANSAS-SL-HCI8-8T-0B module allows for safe and precise measurement of 8 thermocouples (type K) which run on high common mode voltage levels of up to 800 V. Moreover, it is designed to be especially protected (per IP65), thus being particularly well-suited for applications in e-mobility. Imc CANSAS: Signal conditioners and specialized modules.
CANSAS-L-HCI8, imc CANSAS Module - A-Tech. HCI8 - productive testing - imcdataworks.com. Imc CANSAS-HCI8 - imc Test Measurement.
CANSAS-HCI8-T-L - imc CANSAS Module - A-Tech. Imc Cansas - SCI8 Pdf User Manuals. View online or download Imc Cansas - SCI8 Getting Started. Technisches Datenblatt imc CANSASflex HCI8 899 kB Hochisolierende CAN-Messmodule zur Messung von Thermoelementen PT100/1000 und Kleinspannungen auf bis zu 800 V Niveau.  Darüber hinaus ist es besonders geschützt ausgeführt (nach IP65) und damit insbesondere für Anwendungen im Bereich eMobility prädestiniert.
Handbücher Ingersoll Werkzeuge GmbH Deutschland. Datenblätter - imc Test Measurement. CANSAS-HCI8-T-L, imc CANSAS Module The CANSAS-HCI8-T-L module allows safe and precise measurement with 8 thermocouples (Type K) which are mounted on high-voltage components with common mode voltage levels The imc CANSAS-L-HCI8 enables the measurement of low voltage signals riding on a high or indeterminate common mode level Sitemap - produktiv messen - imcadd.ch. CANSAS-L-HCI8, imc CANSAS Module - A-Tech Instruments. HCI8 - www.imc-australia.com.au - productive testing.


Benefits
Benefits
Customers
RBS
KEYBANK
CISCO
FICO
AMADEUS
CA
BOEING
T-SYSTEMS
Competetion
Why Kubernetes?
- #1 Project on github for container orchestration community wise.
- Google has invented it and using it from 15 years
- Redhat is #2 contributor to Kubernetes
- Powers the infrastructure behind Gmail, YouTube, Google Search, Google Compute Engine
- Kubernetes enjoyed more than 15 years of active development and heavy production use
Docker Swarm
- It's easy to use
- Works well with docker.
- Not proven in big environments
- Have issues with networking and persistent storage.
- Community is small, docker controls most of thecontribution/community
- Market favours less concentration of power at the orchestration layer of containers
Working with Microsoft Team Foundation Server(TFS)
- TFS is working with Jenkins for building the application
- Openshift also uses Jenkins for building the application
- Jenkins can used to bridge Openshift with TFS
- TFS 2013 provides GIT also
Useful Links
- "https://developers.openshift.com"
- "https://openshift.com"


Linus > Stallman
This is another paragraph.
Bash
Bash is actually a part of the GNU project, not Linux.
Controversy
Richard Stallman, creator of the GNU Utilities wanted to have
Linux always called by the name GNU/Linux (despite the unpronoucable
mouthfull that is). This name was quickly dropped due to convenience
and lack of consideration for the thousands of other contributions
from other projects outside of the GNU project.
Organizations
Distros
Distro Progeny
| Parent |
Distro |
| Debian |
Debian |
| Ubuntu |
| Ubuntu |
PopOS! |
| Huzzah! |
UNIX-like Operating Systems