# Executor Published articles for Executor. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Ask Your Survey Anything: Building AI Analysis Pipelines with Airflow 3 DevFeed: [Ask Your Survey Anything: Building AI Analysis Pipelines with Airflow 3]() Original publisher: [Read original article]() Author: Apache Airflow Published: 2026-04-15T00:00:00Z Content type: tutorial Language: en Sources: [Apache Airflow Blog]() Topics: [airflow](), [Artificial Intelligence](), [Large Language Model](), [SQL](), [CSV](), [data]() Tags: [ai](), [airflow](), [apache-airflow](), [automated](), [community](), [configuration](), [csv](), [executor](), [llm](), [pipelines](), [providers](), [scheduled](), [sql](), [tutorial]() ### AI overview A tutorial showing how to build interactive and scheduled AI analysis pipelines with Airflow 3. The pipelines use survey CSV data, generate and run SQL queries, validate schemas, and either request human approval or email results automatically. ### Source excerpt The 2025 Airflow Community Survey collected responses from nearly 6,000 practitioners across 168 questions. You can open a spreadsheet and filter, or write SQL by hand. But what if you could just ask a question and have Airflow figure out the query, run it, and bring the result back for your approval? This post builds two pipelines that do exactly that, using the apache-airflow-providers-common-ai provider for Airflow 3. The first pipeline is interactive: a human reviews the question before it reaches the LLM and approves the result before the DAG finishes. The second is scheduled: it downloads fresh survey data, validates the schema, runs the query unattended, and emails the result. If you haven't seen the common.ai provider overview yet, start there for a tour of all the operators. This post goes deep on a concrete end-to-end example. Two Pipelines, One Example File Both DAGs live in example_llm_survey_analysis.py and share the same schema context and datasource configuration. example_llm_survey_interactive: trigger manually, review at both ends: prompt_confirmation -> generate_sql -> run_query -> extract_data -> result_confirmation (HITLEntryOperator) (LLMSQLQuery) (Analytics) (@task) (ApprovalOperator) example_llm_survey_scheduled: runs @monthly, no human in the loop: download_survey -> prepare_csv -> check_schema -> generate_sql -> run_query -> extract_data -> send_result (HttpOperator) (@task) (LLMSchema (LLMSQLQuery) (Analytics) (@task) (@task / Email) Compare) The Data The Airflow Community Survey 2025 CSV has 5,856 rows and 168 columns covering everything from Airflow version and executor type to cloud provider, company size, and AI tool usage. A few highlights from the data: 3,320 respondents identify as Data Engineers 2,032 use AWS as their primary cloud provider for Airflow 1,445 are already running Airflow 3 1,351 say they often use AI tools to write Airflow code Those last two numbers together are part of why this example exists: the people most likely to use th ## Baidu и AI Search Paradigm: мультиагентная структура для интеллектуального поиска информации DevFeed: [Baidu и AI Search Paradigm: мультиагентная структура для интеллектуального поиска информации]() Original publisher: [Read original article]() Author: redmadrobot (red\_mad\_robot) Published: 2025-10-14T18:36:48Z Content type: article Language: ru Sources: [Redmadrobot EN](), [Redmadrobot RU]() Topics: [AI search](), [Artificial Intelligence](), [Large Language Model](), [information retrieval](), [Retrieval Augmented Generation (RAG)]() Tags: [ai](), [ai-search](), [executor](), [information-retrieval](), [llm](), [rag](), [search](), [tag-d89cae10e887](), [tag-e1c0c11ccefc]() ### AI overview The article analyzes Baidu's AI Search Paradigm, a proposed multi-agent architecture for intelligent search built on large language models. It explains how specialized agents coordinate planning, tool use, execution, and answer writing, and contrasts this approach with classical information retrieval, semantic search, learning-to-rank, and RAG systems. ### Source excerpt Аналитический центр red_mad_robot продолжает разбирать ключевые исследования в сфере интеллектуальных систем и генеративного поиска. На этот раз рассказываем про архитектуру AI Search Paradigm от Baidu -- новой системы интеллектуального поиска, построенной на LLM и мультиагентных методах. Читать далее ## How @MainActor works DevFeed: [How @MainActor works]() Original publisher: [Read original article]() Author: Ole Begemann Published: 2022-05-05T13:52:42Z Content type: tutorial Language: en Sources: [Ole Begemann]() Topics: [Swift](), [Concurrency](), [Protocol (disambiguation)]() Tags: [await](), [compiler](), [concurrent](), [executor](), [main-thread](), [protocol](), [swift]() ### AI overview This tutorial explains how Swift's @MainActor works by reimplementing it in simplified form. It describes the roles of global actors and custom executors, including how a custom serial executor can run jobs on the main dispatch queue. ### Source excerpt @MainActor is a Swift annotation to coerce a function to always run on the main thread and to enable the compiler to verify this. How does this work? In this article, I'm going to reimplement @MainActor in a slightly simplified form for illustration purposes, mainly to show how little "magic" there is to it. The code of the real implementation in the Swift standard library is available in the Swift repository. @MainActor relies on two Swift features, one of them unofficial: global actors and custom executors. Global actors MainActor is a global actor. That is, it provides a single actor instance that is shared between all places in the code that are annotated with @MainActor. All global actors must implement the shared property that's defined in the GlobalActor protocol (every global actor implicitly conforms to this protocol): @globalActor final actor MyMainActor { // Requirements from the implicit GlobalActor conformance typealias ActorType = MyMainActor static var shared: ActorType = MyMainActor() // Don't allow others to create instances private init() {} } At this point, we have a global actor that has the same semantics as any other actor. That is, functions annotated with @MyMainActor will run on a thread in the cooperative thread pool managed by the Swift runtime. To move the work to the main thread, we need another concept, custom executors. Executors A bit of terminology: The compiler splits async code into jobs. A job roughly corresponds to the code from one await (= potential suspension point) to the next. The runtime submits each job to an executor. The executor is the object that decides in which order and in which context (i.e. which thread or dispatch queue) to run the jobs. Swift ships with two built-in executors: the default concurrent executor, used for "normal", non-actor-isolated async functions, and a default serial executor. Every actor instance has its own instance of this default serial executor and runs its code on it. Since the serial exec ## Java 9 Flow API: switching threads DevFeed: [Java 9 Flow API: switching threads]() Original publisher: [Read original article]() Author: David Karnok (noreply@blogger.com) Published: 2017-09-21T10:49:00Z Content type: tutorial Language: en Sources: [Akarnokd - Advanced RxJava]() Topics: [Java](), [Java 9](), [API](), [reactive](), [Concurrency](), [Concurrent Programming]() Tags: [asynchronous](), [executor](), [executorservice](), [flow](), [flow-api](), [idea](), [intellij](), [java](), [java-9](), [jvm](), [main-thread](), [observeon](), [publisher](), [thread](), [threading](), [threads]() ### AI overview This tutorial explains how to switch execution between threads in reactive flows using Java 9's Flow API. It compares embedding thread switching in operators with a separate observeOn stage, then outlines an observeOn implementation based on Executor, asynchronous boundaries, bounded queues, and request management. ### Source excerpt Introduction Ensuring certain computations happen on the right thread, usually off the main thread, is a very common development task when dealing with reactive flows. When building up tools for Java 9's Flow API, one can decide to add this thread-switching support to each operator directly - see the range() operator from the start of the series -, or have a standalone stage for this purpose. This is a tradeoff. Inlining thread switching avoids bogging down the source thread like the thread-stealing behavior of most of the queue-drain approach presented so far. A separate operator allows better composition and may even allow working with exotic asynchrony-providing components. The observeOn operator In Java, threading support is provided via the Executor, ExecutorService and ScheduledExecutorService-based API. Executor is is the most basic one of them which only provides a single execute(Runnable) method. This allows creating an Executor from a lambda: Executor trampoline = Runnable::run; Executor swing = SwingUtilities::invokeLater; Executor pool = ForkJoinPool.commonPool(); As the least common denominator, we'll use Executor in defining our observeOn operator: public static Flow.Publisher observeOn( Flow.Publisher source, Executor exec, int prefetch) { return new ObserveOnPublisher<>(source, exec, prefetch); } Crossing an asynchronous boundary requires the temporary storage of an event until the other side can pick it up. The queue-drain approach can provide a nice bounded queue we can size with prefetch. In addition, the so-called stable-prefetch request management (shown in the mapFilter operator before) allows minimizing the overhead of requesting more items. First, let's see the skeleton of the operator's main Flow.Subscriber implementation: static final class ObserveOnSubscriber implements Flow.Subscriber, Flow.Subscription, Runnable { final Flow.Subscriber downstream; final Executor exec; final int prefetch; final Queue queue; F ## Become a Firebase Taskmaster! (Part 3: Wiring up your Tasks) DevFeed: [Become a Firebase Taskmaster! (Part 3: Wiring up your Tasks)]() Original publisher: [Read original article]() Author: Doug Stevenson Published: 2016-09-29T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Android](), [API](), [Java](), [Library](), [Processes]() Tags: [android](), [api](), [best-practices](), [blocking](), [executor](), [firebase](), [java](), [library](), [tutorials]() ### AI overview Part three of a Firebase blog series explains advanced use of the Play services Task API for Android, including creating Tasks with Callable objects and running blocking work on an Executor. ### Source excerpt News, tutorials, and updates from the Firebase team.