Chaos Computer Club - archive feed

This talk shows how to enhance GitOps by putting observability and Service Level Objectives in the center of the deployment process, based on CNCF projects like Argo and Keptn.

GitOps has arrived and shapes today’s way of delivering applications on cloud-native systems. Although GitOps controllers maintain the technical state of an application, there are additional – not necessarily technical – things to consider when deploying applications. For instance, we might want to know if we have already consumed our error budgets before deploying a new service version and make this step a conscious decision. Additionally, we want to ensure that the response time of our service is as expected (and agreed with the customer) before shifting the traffic to a new service version in blue/green or canary deployments.

This session starts with a concise overview of GitOps, the problems it solves, and other things to consider when deploying enterprise-grade applications using GitOps. After this talk, you will know how you can put observability at the center of your deployment process and how this works based on an example with ArgoCD and Keptn.

about this event: https://programm.froscon.org/2022/events/2752.html

This talk is a hands-on introduction to fuzz testing. After a basic introduction to fuzzing we will give a live demonstration of our open source fuzzing tools, supporting C/C++, Java, JavaScript and Go. They will showcase modern state-of-the-art fuzzing approaches and demonstrate the different kinds of bugs one can detect.

To get everyone on board we will take a short tour through the history and fundamentals of fuzzing before we look at the current state of fuzzing including code instrumentation for coverage guided fuzzing and bug detectors. We will find out what kind of bugs and vulnerabilities can be found with these techniques.

We will do this by taking a look on how we use this modern approaches at Code Intelligence (Bonn, https://www.code-intelligence.com/) to make fuzzing as easy as writing unit tests, including demonstrations of our OSS tools Jazzer (https://github.com/CodeIntelligenceTesting/jazzer) and cifuzz (https://github.com/CodeIntelligenceTesting/cifuzz).

about this event: https://programm.froscon.org/2022/events/2772.html

OpenStreetMap (OSM) data has the potential to facilitate bottom-up approach to transport planning which is essential for localized data-driven policy interventions. Given this, OpenInfra project is exploring the potential of OSM data in transport research with a focus on active travel. The exploration showed that currently missing data limits the applicability of OSM data. Nevertheless, we argue that the potential and relevance of OSM data can be demonstrated by recategorizing OSM data to provide more actionable insights to policy-makers. This, therefore, could encourage the uptake of open data leading to more transparent, reproducible, and participatory transport planning.

One of the key domains in which OpenSteetMap (OSM) data has been utilized is transport research [1]. OSM has been used in agent-based transport simulation [2] and routing [3], including cycling [4], walking [5], wheeling [6], and blind pedestrian routing [7]. Another application of OSM data is in transport infrastructure planning. Nelson et al. [8] argue that OSM has the potential to become a primary source of data on infrastructure across the globe.

Regardless of OSM’s potential to become a primary source of data on infrastructure, its potential in active travel infrastructure planning is yet to be realized. One of the potential reasons behind this lag might be linked to the perceived unreliability of open-access crowdsourced data [9]. The quality of OSM has received extensive examination [1] in which the question concerning data completeness plays a significant role because, it is argued, the mappers are not coordinated to guarantee systematic coverage [10]. To address this issue, Barrington-Leigh and Millard-Ball [11] assessed OSM road completeness and found that globally over 80% of roads are mapped. Problematically, however, their assessment focused on roads designed for motor traffic, thus excluding other modes of transport. This gap has been partially addressed by Ferster et al. [12]who examined and compared OSM cycling infrastructure in Canada. They have not, however, considered the infrastructure from the perspective of accessibility. Moreover, there seems to exist no equivalent study using OSM data in the context of pedestrian infrastructure planning.

Nevertheless, open-access crowdsourced data, such as OSM, can support an increasing need for local evidence to inform transport policies. This is important in the context of the UK in which a shift from provision for motorised modes towards more sustainable active modes of travel, such as walking, wheeling, and cycling, takes place [13]. The importance of localizing interventions to meet the needs of local communities has been outlined in both policy [15] and academic [16] papers. A potential way to engage citizens in the decision-making is to encourage “produsage” – a model in which citizens both produce and use data [17].

Acknowledging the potential of OSM to boost citizen participation, OpenInfra project, run at the University of Leeds (UK), aims to address the gap of literature regarding the potential OpenStreetMap in transport research. The project started by examining the existing OSM tags relevant to active travel infrastructure in England with a focus on West Yorkshire, Greater Manchester, Greater London, and Merseyside. The data has been queried using osmextract [18], a package in R, and explored using exploratory data analysis (EDA) approach. A reproducible code containing all the figures discussed here can be found on GitHub: https://github.com/udsleeds/openinfra/tree/main/sotm2022

Given the extensive use of OSM data in transport research, it is not surprising that OSM provides a comprehensive active travel network, yet there is a lack of specification concerning the type of infrastructure that is present (e.g. is it a cycle lane or a cycle track?). For instance, cycleways and footways constitute about 1/3 of all the mapped highways on which one can legally walk, wheel or cycle but only a few percent of the cycleways and footways have tags detailing their type. The data gets even scarcer in the context of accessible infrastructure planning. For example, there is a lot of missing information on the presence and type of kerbs – a street element that might make the movement of a wheelchair user more challenging [19].

The missing data currently limits the use of OSM data in active travel planning, however this does mean that the use of OSM data should be dismissed. Following Nelson et al.’s [8] argument that it is important to make crowdsourced data more actionable, we decided to recategorize OSM data based on Inclusive Mobility (IM) [15], a guide that outlines the best practices in creating inclusive pedestrian infrastructure in the UK. For this, a function has been written (documentation can be found here: https://udsleeds.github.io/openinfra/articles/im_get.html). It takes an OSM dataframe, recategorizes its tags based on the definitions outlined in the guide, and returns an OSM dataframe with new columns to use in further analysis. However, the function provides a simplification of the IM guide for a couple of reasons. The first one could be considered in terms of definitional discrepancies. For instance, the guide defines footways as “pavements adjacent to roads”, yet this is not easily extracted from the OSM in which highway=footway is a generic tag and often there is no further refinement (e.g., sidewalk=*) to determine if it is a pavement adjacent to a road. Another reason is linked to assigned values. For example, the guide identifies six tactile paving surfaces but OSM focuses on the presence/absence of tactile paving, thus limiting how much information can be extracted from the data.

One potential application of the IM function could be to explore the existence and geographic distribution of accessibility indicators, such as the presence of a flush kerb. Yet, more interesting results can be produced by using recategorised OSM data in conjunction with other datasets that would help to improve the understanding of the accessibility of streets. As an illustration for this, an open-access Leeds Central Council Footfall data was used [20]. We reasoned that the locations at which footfall data were collected are heavily used by pedestrians, thus demonstrating the need to ensure inclusive spaces. 5 unique streets were identified, which resulted in 35 linestrings in OSM. Then, a basic index of accessibility, ranging from 0 to 5, was created. For example, if a linestring is classified as a footway, footpath, or implied footway based on the IM guide, then it received 1, otherwise 0. If a flush kerb is mapped, it received 1, otherwise (e.g., not flush or NA), 0 is given. Finally, the values were added and a final index produced. Following this, the highest index score is 2 (19 linestrings), while the rest scored 1. This example does not necessarily show that the streets are inaccessible because the missing data make it hard to make a fair judgement (e.g., in this case not a single linestring has data on kerbs). However, we would argue that this is a space for OSM to produce more readily actionable insights regarding transport infrastructure, especially if joined with other (open) datasets that would help to overcome some of its current data limitations.

The following steps of the OpenInfra project are focused on scaling up. The goal is to produce ‘OSM transport infrastructure data packs’ for transport authorities in England to support the uptake of open-access data, such as OSM, in transport planning. We believe that the utilization of open-access data could make transport planning more transparent, reproducible, and participatory which, consequently, would support an uptake of sustainable modes of travel. OSM specifically has the potential to provide localized insights on the existing transport infrastructure and facilitate more inclusive and accessible transport planning.

about this event: https://2022.stateofthemap.org/sessions/CEMMTQ/

Lighting talks registered during the State of the Map conference.

## Offline Web Mapping Server UNVT Portable

The United Nations Vector Tile Toolkit.

_by Shogo Hirasawa, Taichi Furuhashi_

## Liaising OpenStreetMap (OSM) Community and Research Community with the Policy Makers: Reducing the Data Gap in Disaster Management

_by Airin Akter, Shraddha Sharma_

## Unique Mappers Network: The OpenStreetMap Community NGO in Nigeria

_by Victor N. Sunday, Nwinkua Dumdibabari_

## NOAH (Nationwide Operational Assessment of Hazards) Website, revamped!

_by Feye Andal_

about this event: https://2022.stateofthemap.org/sessions/NNKX8K/

Null Island is where the prime meridian meets the equator at (0,0) longitude and latitude. While Null Island is a fictitious, dimensionless, point object, its existence stimulates vigorous debate making it worthy of serious consideration. Many examples exists illustrating how Null Island impacts OSM discourse. Our study considers what the geographic oddity of Null Island means for OSM. The main contribution is a structured knowledge-based resource facilitating understanding of Null Island’s impact on OSM. This socio-technical and philosophical investigation of Null Island can become a catalyst for deeper discussions and debates in OSM around mapping practices.

Null Island refers to the location where the prime meridian meets the equator at 0o longitude and 0o latitude. With coordinates (0, 0), it is the origin of the WGS84 geographic coordinate system. It has been argued that Null Island can be considered a real place that is a product of our digital age [1]. Null Island’s significance comes from the fact that it is erroneously associated with large amounts of geographic data that spans across geo-social media, location-based services and map databases. Even though Null Island is a fictitious, dimensionless, point object, its existence stimulates debate that elevates Null Island into a global issue worthy of serious consideration (a detailed description of associated issues is given in [1]). Members of the OpenStreetMap (OSM) project often interact with this location in various ways, and therefore understanding what Null Island means for OSM is relevant. We can find several examples of Null Island affecting OSM, such as a recent debate that arose in the talk mailing list in January 2022 with the title “Was the deletion of Null Island reasonable?” [2], where contributors argued for or against the deletion of Null Island. In addition, a web search for the term “Null Island” on the openstreetmap.org domain [3] reveals that Null Island was mentioned across the entire OSM ecosystem, including mailing lists, forums, user diaries, notes, features, changesets, wiki pages, help articles, blogs and even the Ruby on Rails codebase of the OSM website uses Null Island for testing (https://tinyurl.com/OSM-Ruby-Null). These suggest that Null Island already has a widespread reach within the OSM project.

The purpose of this study is to consider both qualitatively and quantitatively what the geographic oddity of Null Island means for OSM. No research works exist which tackle this issue in depth. Previous studies mentioning Null Island do so in a simplistic way and use the term to refer to the (0, 0) location (see e.g. [4]–[6]). Only a few studies recognize it as a special location and unique phenomenon ([7], [8]), and to our knowledge, only one study tackles the issue in depth [1]. In addition to contributing a robust academic study of Null Island, this work will produce a structured knowledge-based resource for the community to understand Null Island’s impact on OSM.

Building on [1] we investigate the various ways Null Island is represented in the OSM project subsequently contributing an evidence-based narrative history on the evolution of Null Island. This includes the qualitative review of various OSM communications channels (e.g. mailing lists, discussion boards and wikis) for mentions and references to Null Island. We believe these channels help provide insights about how the OSM community contextualizes, describes and deals with Null Island. The history of special map features related to Null Island, such as node #1 (https://tinyurl.com/osm-first-node) and the node located at (0, 0) (https://tinyurl.com/OSM-Center) will also be reviewed to illustrate what actions the OSM community took in terms of adding and removing Null Island to the database. In addition to these qualitative approaches, we utilize the ohsome API [9] to extract and analyze map edits made on or near Null Island, which provides a quantitative way to assess the frequency of erroneous data added to OSM near (0, 0) as well as the semantics of such data.

Interesting patterns have already emerged from the preliminary analysis of data. The most recent mailing list debate mentioned above [2] can be summarized as follows. 17 individuals contributed 45 e-mails to the discussion between January 3 and January 10, 2022. One of the (very few) rules of OSM is that data should be verifiable, meaning that others can visit the real location of a map object and see for themselves if the data is correct. This is also known as the “ground-truth rule” [10]. Null Island as a fictional place violates this rule, therefore a popular stand in the debate is that it should not be part of OSM. This was explicitly expressed by five individuals, including a member of the authoritative Data Working Group. A counter argument is that Null Island is fundamentally similar to localities and neighborhoods, that might not exist as political or physical entities, but are known only informally to a group of people inhabiting that area. In this sense, Null Island is a place that exists in the collective consciousness of people and the name refers to the same geographic area. This justifies tagging the (0, 0) location as place=locality and name=”Null Island” in OSM. This view was explicitly supported by seven members on the mailing list. The remaining five individuals that contributed to the discussion did not take a clear stand on whether to remove or keep Null Island, but have provided arguments both for and against the deletion of it.

The full history of OSM data was extracted from the elementsFullHistory endpoint of the ohsome API [9] within the geographic bounding box defined by the southwest point of (-0.001, -0.001) and the northeast point of (0.001, 0.001) between January 1, 2012 and January 1, 2022. During this 10-year-long period, a feature was added, deleted or modified every three days on average within this bounding box, resulting in 1323 unique features (nodes, ways or relations). In addition, map Notes as well as GPS traces are also constantly being created, which makes Null Island and its surrounding a busy area in terms of OSM data activity.

Null Island is a socio-technological concept that has only been sparsely present in the GIScience literature so far. Our novel approach highlights how a seemingly lighthearted topic like Null Island can generate serious debates that are technological, social and even philosophical in nature. OSM and Null Island have a long tradition together with sometimes heated mapping debates resurfacing from time to time with no apparent resolution in sight. While resolving these debates is entirely in the hands of the OSM community, our research contributes to the potential resolution of them in a meaningful way by providing a factual, detailed, and accurate account of Null Island in OSM. Furthermore, while Null Island is potentially the most prominent example of a fictional place affecting maps and mapping practices, other examples also exist. For example, the most remote location on Earth, Point Nemo (which is the point in the ocean that is farthest from land) [11] is also present in OSM (https://tinyurl.com/OSM-PointNemo). Our OSM specific investigations together with a more general introduction of Null Island from both technological and social perspectives presented in [1] will help demystify the abstract concept of a fictional place that is present in real databases. Increased understanding will potentially help OSM members resolve mapping debates about “real fictional places”. Discussion around Null Island and other fictional places is unlikely to end with this work. Our work will contribute in a technical, socio-technical and philosophical way to the Null Island story in OSM with the potential to become a catalyst for further discussions related to wider debates in OSM around mapping practices.

about this event: https://2022.stateofthemap.org/sessions/LTA77E/

Sparked by concerns about OpenStreetMap's role in how the public accesses and recreates on protected lands, OpenStreetMap US volunteers, navigation app developers, national agencies and public land managers formed the OpenStreetMap US Trails Working Group in 2021. Bringing together a diversity of perspectives on trail mapping practices, trail safety, and protecting the environment, this group is working to address on-the-ground challenges, tagging schemes, authoritative data, and other topics related to mapping trails in OSM. Learn how this group is collaboratively developing solutions for responsible trail mapping in OpenStreetMap.

about this event: https://2022.stateofthemap.org/sessions/CUV9H7/

Urban green spaces serve people for active and passive recreation. On the basis of OpenStreetMap data, suitable green spaces are to be derived in order to incorporate them as recreation destinations in a location-based service (the “meinGruen” app) as polygons. The modelling approach focuses on activity-related barriers in the context of urban green, transitions between different land use classes, and public accessibility. The case study was implemented for the city of Dresden in Germany.

In addition to important ecosystem services such as clean air or local climate regulation, green spaces provide peace and recreation, contributing to a good quality of life for the population. In high-density urban areas, publicly accessible green spaces are used for a variety of recreational activities, which has become even more important, not least because of the COVID-19 pandemic [1]–[4]. In this context, the research project "Information and Navigation on Urban Green Spaces in Cities - meinGruen" examined publicly accessible green spaces with regard to a variety of criteria in order to assess their suitability for the pursuit of leisure activities, such as going for a walk or playing soccer [3], [5], [6]. The aim of this study is to derive a suitable polygon dataset to describe the spatial distribution of publicly accessible urban green spaces. The presented approach favors the use of OpenStreetMap data and intrinsic knowledge. Advantages of the use of OpenStreetMap data are the global availability, the often high completeness in urban areas as well as the unified open data license ODbL 1.0. In this way, problems with data availability and heterogeneity due to different responsible authorities can be avoided. Ludwig et al. [7] describe an approach to mapping public green spaces based on OpenStreetMap and Sentinel-2 satellite imagery in which barriers and land use changes are considered based on a priori (expert knowledge) assumptions for polygon generation. In the approach presented here, spatial delimitation is to be refined by describing barriers by probability values. The term "barrier" is first analyzed in an interdisciplinary way in order to then work out its meaning for the spatial delimitation of a green space. Here, barriers describe the action space of a recreational activity. While there are a number of object types (such as walls, fences, rivers, roads or railroad lines) can be assumed to be barriers with certainty, there are others (such as paths or the change of land use) for which knowledge is still lacking. The study area includes the city of Dresden in Germany, plus a buffer of five kilometers. OpenStreetMap represents the main data source. For training and validation, official cadastral data (ALKIS) as well as a dataset on cadastral parcels owned by the city of Dresden were used.

The methodology consists of six steps: First, according to defined rules, types of barriers were extracted from OpenStreetMap data. Second, we derived a land use layer without overlaps and holes from OpenStreetMap. Here, two options were compared regarding different target schemes for land use classification. Third, a mapping in terms of a “ground-truth“ in selected areas in Dresden followed in order to be able to evaluate the existence of a barrier on site for the extracted paths and changes of land use. Fourth, generic probabilities for the existence of a barrier were determined based on path type or land use change type. Fifth, a polygon mesh was created by applying thresholds to the determined barrier probabilities. Sixth, the generated polygons were enriched with attributes on the number of green space-related POI, such as benches, trash cans, or trees. Models for "greenness" and "accessibility" are thereby trained.

For the technical implementation mainly Docker, PostgreSQL/ PostGIS, Python (Geopandas, Scikit-Learn) and Jupyter Notebook were used. Data import was performed by osm2pgsql and ogr2ogr. For mapping we used the app QField.

Land use layers were successfully generated for the study area using a residual class. The results indicated that the land use classification according to the area scheme of the IOER-Monitor (option B) has a higher thematic accuracy with a maximum of 33 classes (433 original OSM tags were assigned) than the option A based on a classification according to osmlanduse.org/ Schultz et al. (up to 13 classes, based on 61 OSM tags) [8], [9]. The classes of arable land (A: 28.40% / B: 28.06% share of area) as well as forest (A: 21.81% / B: 23.33%) are dominant in both variants. While the residual class takes up 6.29% of the area in option A, it is only 4.88% in option B. For the “ground-truth”, a total of approximately 82.3 km of paths (with 408 line objects) and approximately 64.2 km of land use changes (1720 line objects) were evaluated for the presence of a barrier in two selected areas in Dresden. The land use changes are based on variant B. Data were collected on 61 different land use transitions and four different trail types. While bike lanes can be safely assumed to be a barrier, the "track" (96.8%), "footway" (92.7%), and "path" (86.0%) trail types have a slightly lower barrier probability. Among land use transitions, the forest-meadow (12.6%), meadow-sports facility (22.8%), meadow-park (24.6%), and forest-grassland (26.7%) transitions have the lowest barrier probabilities. Together with the barriers assumed to be safe at the beginning, a line pool is formed, from which different polygon meshes are generated based on different intervals for the barrier probability (p ≥ 0%; p ≥ 20%; p ≥ 40%; p ≥ 60%; p ≥ 80%; p = 100%). The lower the probability threshold, the higher the number of polygons created (whose area decreases). For the "accessibility" model, the number of benches, trash cans, public toilets and public internet were considered per polygon. The logistic regression achieved 76.7% accuracy here, similar to a Support Vector Classifier (SVC). The "greenness" model is based on number of benches, picnic tables, trees, and trash cans per polygon. The accuracy is about 92.3% (for logistic regression and also Support Vector Classifier).

This work successfully demonstrates a new approach to derive publicly accessible green spaces based on OpenStreetMap data considering different qualities of barriers in contact of green spaces. Based on the examined barrier probability of path types and land use transitions, more realistic spatial delineations of green spaces were made possible. The chosen approach is globally applicable due to the use of OpenStreetMap. In each case, locally prevailing climatic and cultural influences must be taken into account. The knowledge collected here can be applied in the Central European region. For other areas, a renewed “ground-truth” may have to be carried out on site. The schematic transformation of the land use into the area scheme of the IOER-Monitor leads to a reduction of classes compared to the original data. In addition to benefits in capturing barrier probability, it also simplifies comparability and transferability. Thus, other data could also be migrated into this scheme. The determined barrier probabilities correspond to the expectations. The polygon generation based on different barrier probabilities allows here a differentiated setting of the desired action space for the relevant leisure activities. The quality of the trained models is good, but can be improved. A variety of additional features can be calculated for each potential green space (polygon), such as path network density or density of path network intersections (see also Ludwig et al. [7]). Questions about the perception and use of green spaces can also be part of interdisciplinary research in the future.

about this event: https://2022.stateofthemap.org/sessions/ASADTB/

What must be mapped to make routing for prams and wheelchairs practical? Three years ago, the local meet-up in Dortmund, Germany, started a campaign to make step-free routing available for the general public.

The lessons learned mean that such routing is possible, but there is a lot missing to map - both in Dortmund and in all other parts of the world.

Map the essential where fellow mappers are sparse. And codify the full ground truth where the passion allows it. I hope to encourage mappers for the quest to get their neighbourhood ready for wheelchairs, prams and all the other pedestrians!

Routing for pedestrians is a much broader challenge than the well-known car routing.

Cars all over the world are mostly uniform, but pedestrians vary widely in their capabilities.

This means that a lot of details that a sportive person might not even notice can be literally a roadblocker for people with prams, for wheelchair users or simply lesser-abled people with not enough strength for a complete stairway.

Becoming a father has been a good opportunity to check in practice what is and what is not feasible for a pedestrian with a pushed vehicle. It turns out that the first step is to get aware of the various kinds of obstacles that get in the way. Beside the obvious steps and kerbs, there are impassable surfaces, too narrow or too steep sections. Or simply sidewalks missing completely on the ground.

As of now, OpenStreetMap data does not even suffice to figure out where one or both sidewalks actually exist. This puts into perspective the discussions about how to map best details of both detached ways and sidewalks. A couple of tagging approaches are compared to allow educated guesses which level of detail will allow for good results rather in weeks and months than in years or decades. I even dare to give suggestions what tagging practices we should additionally adopt to be able to map faster.

The background of this talk is an initiative from the Dortmund meet-up: For the large event Kirchentag 2019, we mapped at least the city center sufficiently well for wheelchair mapping. The whole city with its 1500 km streets has turned out to be simply too much. Given that a city with a local meet-up is in a relatively good position to be mapped, it was no surprise that also elsewhere the data is simply not yet good enough for wheelchair routing. The hope is that simple suggestions what helps is getting more traction than a sophisticated mapping hierarchy.

about this event: https://2022.stateofthemap.org/sessions/LKZYJ7/

Barrierefreiheit für alle, immer und überall, zumindest in OSS, darum soll es in dem Vortrag gehen. Wer profitiert von gut nutzbarer Software? Welche Prozesse führen zu mehr Barrierefreiheit? Was können wir tun, um accessibility by design zu erreichen?

Durch die zunehmende Nutzung von OSS auf dem Desktop an Schulen und Hochschulen wie im öffentlichen Dienst in Norddeutschland, werden immer mehr Mängel bei der Zugänglichkeit sichtbar. Bisher werden sie meist durch individuelle Workarounds oder nachträgliches Flicken versucht zu beheben. Meist nur mit mäßigem Erfolg, der durch das nächste Update wieder zunichte gemacht wird.

Der Vortrag möchte daher zu einem grundlegenden Umdenken auffordern, zu einem umfassenden Verständnis von Barrierefreiheit als Selbstverständlichkeit.

about this event: https://programm.froscon.org/2022/events/2781.html

Du brauchst eine kleine plattformunabhängige Anwendung? Du kannst Python lesen und schreiben? Dann probiere doch mal web.py, ein kleines Web-Framework mit sehr niedriger Einstiegshürde! Mit wenigen Codezeilen sind auch komplexere dynamische Web-Anwendungen realisierbar, die alles mitbringen, um sie autark unter jedem gängigen Betriebssystem betreiben zu können. Der Vortrag erklärt die ersten Schritte.

Zur schnellen Erstellung einer kleinen dynamischen Webseite, muss man nicht unbedingt auf einen der schwergewichtigen Webframeworks zurückgreifen [1]. Denn um dort zum Ziel zu kommen, ist meist eine längere Einarbeitszeit, die man mit dem Lesen von Dokumentationen und dem Ausprobieren von Tutorials verbringt, vorprogrammiert. Die Gefahr dabei auf halben Weg aufzugeben, ist recht hoch.

Auf der Suche nach schlanken Alternativen, stolperte der Autor dieses Vortrages über einen etwas älteren Artikel im Linux Magazin 08/2006 [2]. Es wurde web.py, damals noch aus einer einzigen Python-Datei bestehend und in der Version 0.138 vorliegend, vorgestellt. Vor allem die 10 Zeilen Python-Code für ein „Hello World“, welche dann nicht mal einen installierten Webserver-Dienst auf dem Entwicklungsrechner benötigten, waren sehr beeindruckend. Und auch wichtig: die Entwick­lung von web.py ist nicht im Jahr 2006 stehen geblieben, wie ein Blick auf die Projektseite [3] be­weist. Also die ideale Spielwiese für einen angehenden (oder auch fortgeschrittenen) Python-Pro­grammierer.

Neben den üblichen Dingen eines Webframworks, wie z.B. der Generierung von validem HTML-Code, Session- und Cookie-Managment sowie HTML-Templates, bringt web.py noch einige andere interessante Dinge mit. An vorderster Stelle stehen dort sicherlich solche Features wie z.B.:

• komfortables URL-Handling

• Generierung von Input-Forms und einfache Validierung der Eingaben

• integrierte Anbindung an SQLite-, MySQL- und Postgres-Datenbanken

• Unterstützung beim Debuggen der Web-Anwendungen durch detaillierte Fehlerausgaben

• in die Anwendung integrierter Webserver

• die Erstellung der Web-Anwendung erfolgt hauptsächlich in nativen Python (mit allen Vor­teilen, wie z.B. das Einbinden der unzählig verfügbaren Python-Module)

Der Vortrages gibt eine Einführung in die Programmierung von dynamischen Webseiten mit dem Python-Modul web.py. Dabei wird auch verraten, welches Anwendungsszenario eigentlich den Aus­schlag gegeben hatte, sich mit diesem Thema zu beschäftigen.

about this event: https://programm.froscon.org/2022/events/2747.html