Top 50 Rising Programming Technologies

(Based on Stack Overflow tag count and upward trend as proportion of Stack Overflow questions. So it might just be the 50 most difficult-to-learn rising programming technologies!)

The List

  1. Python

    Interpreted high-level programming language for general-purpose programming

  2. React

    JavaScript library for building user interfaces

  3. Laravel

    PHP web framework (“for web artisans”)

  4. Pandas

    Data structures and data analysis tools for Python

  5. TypeScript

    Superset of JavaScript that adds static typing (“JavaScript that scales”)

  6. Amazon Web Services

    On-demand cloud computing platform

  7. API

    The interface between two programs.

  8. Azure

    Cloud computing service

  9. Powershell

    Commandline shell and associated scripting language

  10. Firebase

    Mobile and web application development platform

  11. Selenium
  12. Spring Boot
  13. Docker
  14. React Native
  15. DataFrame
  16. Unity 3D
  17. Elasticsearch
  18. Matplotlib
  19. Go
  20. Jenkins
  21. Selenium Web Driver
  22. Gradle
  23. Machine Learning
  24. Amazon S3
  25. vue.js
  26. ggplot2
  27. Flask
  28. ASP.NET Core
  29. npm
  30. Webpack
  31. Mongoose
  32. tkinter
  33. Google Apps Script
  34. Web Scraping
  35. Spring Security
  36. filter
  37. https
  38. Woo Commerce
  39. Xamarin Forms
  40. Web Socket
  41. Android Recycler View
  42. Kotlin
  43. Redux
  44. Google Sheets
  45. Excel Formula
  46. SASS
  47. Hive
  48. Java 8
  49. Redis
  50. CMake

The Top Ten

Chart of the top 10:

The Top Ten Without Python

Python dwarfs everything else so here’s a look without Python:

The Nearly Men

These tags were eliminated from the list solely on the basis of a 2018-only downward trend: R (would have been 2nd), Node.js (2nd), PostgreSQL (4th), numpy (12th), Express (14th), Apache Spark (14th), Tensorflow (18th), nginx (20th), Github (21st), Amazon EC2 (31st), ECMAScript 6 (39th), ffmpeg (46th)

Programming Languages That Make the List

  1. Python
  2. TypeScript
  3. Go
  4. Kotlin

A Rising Python Lifts All Python Libraries

python-3.x actually makes second place on the list but I rolled it into Python rather than make a redundant entry.

Django and Django Models were eliminated from the list despite being on an upward trend because they have not yet exceeded their previous peak in 2010. This resurgence, Pandas in fourth place and the presence of tkinter on the list speaks to the general rising of Python.


Load all tags on StackOverflow by count descending.

Put each of them into StackOverflow Trends and judge by eye if they are currently trending up.

Where Do These Technologies Sit in the Overall List?

In the whole list of tags by count Python is sixth overall, Firebase (10th place in this list) is 90th, and CMake (50th) is 442nd.

Minimal Implementation of graphql-kotlin

This will bring up a GraphQL endpoint at http://localhost:8080/graphql and the GraphiQL query tool at http://localhost:8080/graphiql if you run ./gradlew bootRun


plugins {
    id("io.spring.dependency-management") version ("1.0.6.RELEASE") // Pull in dependencies automatically.
    id("org.jetbrains.kotlin.jvm") version ("1.3.10")
    id("org.jetbrains.kotlin.plugin.spring") version ("1.3.10")
    id("org.springframework.boot") version ("2.1.0.RELEASE")

tasks.withType( {
    kotlinOptions {
        freeCompilerArgs = listOf("-Xjsr305=strict") // Enable strict null safety.
        jvmTarget = "1.8"

repositories {

dependencies {
    implementation("com.expedia.www:graphql-kotlin:0.0.23") // Generate GraphQL schema directly from code.
    implementation("com.graphql-java-kickstart:graphiql-spring-boot-starter:5.1") // Get the /graphiql page for free.


package {yourpackagehere}

import com.expedia.graphql.schema.SchemaGeneratorConfig
import com.expedia.graphql.TopLevelObjectDef
import com.expedia.graphql.toSchema
import com.fasterxml.jackson.module.kotlin.KotlinModule
import graphql.schema.GraphQLSchema
import graphql.schema.idl.SchemaPrinter
import graphql.servlet.GraphQLErrorHandler
import graphql.servlet.GraphQLInvocationInputFactory
import graphql.servlet.GraphQLObjectMapper
import graphql.servlet.GraphQLQueryInvoker
import graphql.servlet.ObjectMapperConfigurer
import graphql.servlet.SimpleGraphQLHttpServlet
import javax.servlet.http.HttpServlet
import org.slf4j.LoggerFactory
import org.springframework.boot.autoconfigure.SpringBootApplication
import org.springframework.boot.runApplication
import org.springframework.boot.web.servlet.ServletRegistrationBean
import org.springframework.context.annotation.Bean

class Application {
    private val logger = LoggerFactory.getLogger(

    fun schema(): GraphQLSchema {
        val schemaConfig = SchemaGeneratorConfig(
            supportedPackages = listOf("{yourpackagehere}"),
            topLevelQueryName = "YourQuery",
            topLevelMutationName = "YourMutation"

        val schema = toSchema(
            queries = listOf(TopLevelObjectDef(YourQuery())),
            mutations = emptyList(),
            config = schemaConfig
        return schema

    fun graphQLObjectMapper(): GraphQLObjectMapper = GraphQLObjectMapper.newBuilder()
            .withObjectMapperConfigurer(ObjectMapperConfigurer { it.registerModule(KotlinModule()) })
            .withGraphQLErrorHandler(GraphQLErrorHandler { it })

    fun graphQLServlet(
        invocationInputFactory: GraphQLInvocationInputFactory,
        queryInvoker: GraphQLQueryInvoker,
        objectMapper: GraphQLObjectMapper
    ): SimpleGraphQLHttpServlet = SimpleGraphQLHttpServlet.newBuilder(invocationInputFactory)

    fun graphQLServletRegistration(graphQLServlet: HttpServlet) = ServletRegistrationBean(graphQLServlet, "/graphql")

fun main(args: Array) {

Chart.js Charts from HTML Data Attributes

Example HTML

    data-labels="[&quot;White&quot;, &quot;Blue&quot;, &quot;Black&quot;, &quot;Red&quot;, &quot;Green&quot;, &quot;Colorless&quot;]" 
    data-series="[13, 11, 5, 2, 2, null]"></canvas>


$('.chart').each(function () {
    var id = $(this).attr("id"),
        type = $(this).data("type"),
        labels = $(this).data("labels"),
        series = $(this).data("series"),
        options = $(this).data("options"),
        ctx = this.getContext("2d");
    new Chart(ctx, {
        'type': type,
        'data': {
            labels: labels,
            datasets: [{
                data: series
        options: options

1205, ‘Lock wait timeout exceeded; try restarting transaction’

Recently we had an issue with very long-lived locks on certain rows in MySQL/MariaDB. Certain people (each row representing a person) would get locked out of updates for hours at a time. Using Flask/uWSGI/MariaDB/mysqldb.

SHOW ENGINE INNODB didn’t report any recent deadlocks or other issues but SHOW PROCESSLIST showed some very long sleeping processes and when these were killed the locks would go away. SELECT trx_started, trx_mysql_thread_id FROM information_schema.innodb_trx t JOIN information_schema.processlist p ON t.trx_mysql_thread_id =; was also interesting showing some unclosed transactiosn that corresponded.

We seem to have cleared the problem up by explicitly closing the database connection on teardown_request.

Atomic Transactional Replacement of a Table in MySQL

Even with AUTOCOMMIT off a DROP TABLE or CREATE TABLE statement will cause an implicit commit in MySQL.

So if you drop your table of (say) aggregated data and then create a new one even if you’re theoretically in a transaction there will be time when clients of the database see no table and time when they see an empty table.

The solution is to use RENAME TABLE.

CREATE TABLE replacement_table (...) AS SELECT ... FROM ...;
CREATE TABLE IF NOT EXISTS current_table (id INT); -- Just in case this is the first run and the table doesn't exist yet so RENAME TABLE doesn't fail.
RENAME TABLE current_table TO old_table, replacement_table TO current_table;

No client of the database will ever see a database that doesn’t contain an existing and populated current_table.

Sharing Terminal kill-ring with system clipboard on OS X

I managed to get vim in the terminal using the system clipboard by using a version compiled with +clipboard and using set clipboard=unnamed in .vimrc. But I wanted to go one further and have the last entry in my emacs-like kill-ring in bash go to the system clipboard too. So that when I hit Ctrl-k on the commandline I can Cmd-v that text into my text editor (or anywhere). Turns out this is pretty tricky.

The best solution I have so far is courtesy of user3439894 on the Apple Stack Exchange. It requires me to use Ctrl-Shift-k to kill instead of Ctrl-k but otherwise does what I want pretty well. You can find the gory details at

Subsetting Fonts

On we use a bunch of unicode symbols for everything from pagination (←→) to tagging external links (↑) to indicating that a specific card has a bug (🐞).

Lots of these symbols are present in the glyphs of our main body text font. But some are not. These rely on the system of the viewer to display. Sometimes they look ugly and sometimes they aren’t present at all and get the ugly square box treatment.

To work around this I want to supply a font that provides versions of the glyphs that aren’t present in the base font. But something fonts like Symbola that provide complete coverage of unicode start at about 1.5MB and go up. That’s a high price to pay for a handful of symbols!

One solution is font subsetting. You can make a version of a font that only contains the symbols you are going to use. It will be much smaller. Our subsetted version of Symbola is more like 15KB as an inline woff in the CSS file. A big saving.

You can subset fonts online very simply with the super useful Transfonter.

Speeding up pystache

This unfortunately won’t do much good as a PR because the project seems to be abandoned but you can get really big speedups in pystache by caching templates and (particularly) parsed templates. Especially if you are using a lot of partials.

It looks something like this:

# Subclass pystache.Renderer to provide our custom caching versions of pystache classes for performance reasons.
class CachedRenderer(pystache.Renderer):
    def _make_loader(self) -> pystache.loader.Loader:
        return CachedLoader(file_encoding=self.file_encoding, extension=self.file_extension, to_unicode=self.str, search_dirs=self.search_dirs)

    def _make_render_engine(self) -> pystache.renderengine.RenderEngine:
        resolve_context = self._make_resolve_context()
        resolve_partial = self._make_resolve_partial()
        engine = CachedRenderEngine(literal=self._to_unicode_hard, escape=self._escape_to_unicode, resolve_context=resolve_context, resolve_partial=resolve_partial, to_str=self.str_coerce)
        return engine

# A custom loader that acts exactly as the default loader but only loads a given file once to speed up repeated use of partials.
# This will stop us loading record.mustache from disk 16,000 times on /cards/ for example.
class CachedLoader(pystache.loader.Loader):
    def __init__(self, file_encoding: Optional[str] = None, extension: Optional[Union[str, bool]] = None, to_unicode: Optional[StringConverterFunction] = None, search_dirs: Optional[List[str]] = None) -> None:
        super().__init__(file_encoding, extension, to_unicode, search_dirs)
        self.templates: Dict[str, str] = {}

    def read(self, path: str, encoding: Optional[str] = None) -> str:
        if self.templates.get(path) is None:
            # look in redis using modified date on filesystem of path
            self.templates[path] = super().read(path, encoding)
            # write to redis
        return self.templates[path]

# If you have already parsed a template, don't parse it again.
class CachedRenderEngine(pystache.renderengine.RenderEngine):
    # pylint: disable=too-many-arguments
    def __init__(self, literal: StringConverterFunction = None, escape: StringConverterFunction = None, resolve_context: Optional[Callable[[ContextStack, str], str]] = None, resolve_partial: Optional[StringConverterFunction] = None, to_str: Optional[Callable[[object], str]] = None) -> None:
        super().__init__(literal, escape, resolve_context, resolve_partial, to_str)
        self.parsed_templates: Dict[str, pystache.parsed.ParsedTemplate] = {}

    def render(self, template: str, context_stack: ContextStack, delimiters: Optional[Tuple[str, str]] = None) -> str:
        if self.parsed_templates.get(template) is None:
            # look in redis
            self.parsed_templates[template] = pystache.parser.parse(template, delimiters)
            # store in redis
        return self.parsed_templates[template].render(self, context_stack)

Blossom Algorithm in PHP

Following the Swiss Pairings Algorithm I wrote in python using weighted maximum matching I ported the blossom algorithm code to PHP.

This is a direct conversion of Joris van Rantwijk’s python code with the same tests and the same output.

The algorithm is taken from “Efficient Algorithms for Finding Maximum Matching in Graphs” by Zvi Galil, ACM Computing Surveys, 1986. It is based on the “blossom” method for finding augmenting paths and the “primal-dual” method for finding a matching of maximum weight, both due to Jack Edmonds.

Some ideas came from “Implementation of algorithms for maximum matching on non-bipartite graphs” by H.J. Gabow, Standford Ph.D. thesis, 1973.

A C program for maximum weight matching by Ed Rothberg was used extensively to validate this new code.