docs/code-comparisons/_dagster_snippets/dagster_dataflow.py

from dagster import AssetExecutionContext, MetadataValue, asset, MaterializeResult

@asset
def topstory_ids() -> None:
    newstories_url = "https://hacker-news.firebaseio.com/v0/topstories.json"
    top_new_story_ids = requests.get(newstories_url).json()[:100]

    os.makedirs("data", exist_ok=True)
    with open("data/topstory_ids.json", "w") as f:
        json.dump(top_new_story_ids, f)
        
@asset(deps=[topstory_ids])
def topstories(context: AssetExecutionContext) -> MaterializeResult:
    with open("data/topstory_ids.json", "r") as f:
        topstory_ids = json.load(f)

    results = []
    for item_id in topstory_ids:
        item = requests.get(
            f"https://hacker-news.firebaseio.com/v0/item/{item_id}.json"
        ).json()
        results.append(item)

        if len(results) % 20 == 0:
            context.log.info(f"Got {len(results)} items so far.")

    df = pd.DataFrame(results)
    df.to_csv("data/topstories.csv")

    return MaterializeResult(
        metadata={
            "num_records": len(df),
            "preview": MetadataValue.md(df.head().to_markdown()),
        }
    )
    
@asset(deps=[topstories])
def most_frequent_words() -> MaterializeResult:
    stopwords = ["a", "the", "an", "of", "to", "in",
                 "for", "and", "with", "on", "is"]
    topstories = pd.read_csv("data/topstories.csv")

    word_counts = {}
    for raw_title in topstories["title"]:
        title = raw_title.lower()
        for word in title.split():
            word = word.strip(".,-!?:;()[]'\"-")
            if cleaned_word in stopwords or len(cleaned_word) < 0:
                continue
            
            word_counts[cleaned_word] = word_counts.get(word, 0) + 1

    top_words = {
        pair[0]: pair[1]
        for pair in sorted(
            word_counts.items(), key=lambda x: x[1], reverse=True
        )[:25]
    }

    plt.figure(figsize=(10, 6))
    plt.bar(list(top_words.keys()), list(top_words.values()))
    plt.xticks(rotation=45, ha="right")
    plt.title("Top 25 Words in Hacker News Titles")
    plt.tight_layout()

    buffer = BytesIO()
    plt.savefig(buffer, format="png")
    image_data = base64.b64encode(buffer.getvalue())

    md_content = f"![img](data:image/png;base64,{image_data.decode()})"

    with open("data/most_frequent_words.json", "w") as f:
        json.dump(top_words, f)

    return MaterializeResult(
        metadata={"plot": MetadataValue.md(md_content)}
    )

@asset
def signups(hackernews_api: DataGeneratorResource) -> MaterializeResult:
    signups = pd.DataFrame(hackernews_api.get_signups())

    signups.to_csv("data/signups.csv")

    return MaterializeResult(
        metadata={
            "Record Count": len(signups),
            "Preview": MetadataValue.md(signups.head().to_markdown()),
            "Earliest Signup": signups["registered_at"].min(),
            "Latest Signup": signups["registered_at"].max(),
        }
    )