effect-patterns-streams

Effect-TS Patterns: Streams

This skill provides 8 curated Effect-TS patterns for streams. Use this skill when working on tasks related to:

• streams
• Best practices in Effect-TS applications
• Real-world patterns and solutions

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🟢 Beginner Patterns

Stream Pattern 1: Transform Streams with Map and Filter

Rule: Use map and filter combinators to transform stream elements declaratively, creating pipelines that reshape data without materializing intermediate results.

Good Example:

This example demonstrates transforming a stream of raw data through multiple stages.

import { Stream, Effect, Chunk } from "effect";

interface RawLogEntry {
  readonly timestamp: string;
  readonly level: string;
  readonly message: string;
}

interface ProcessedLog {
  readonly date: Date;
  readonly severity: "low" | "medium" | "high";
  readonly normalizedMessage: string;
}

// Create a stream of raw log entries
const createLogStream = (): Stream.Stream<RawLogEntry> =>
  Stream.fromIterable([
    { timestamp: "2025-12-17T09:00:00Z", level: "DEBUG", message: "App starting" },
    { timestamp: "2025-12-17T09:01:00Z", level: "INFO", message: "Connected to DB" },
    { timestamp: "2025-12-17T09:02:00Z", level: "ERROR", message: "Query timeout" },
    { timestamp: "2025-12-17T09:03:00Z", level: "DEBUG", message: "Retry initiated" },
    { timestamp: "2025-12-17T09:04:00Z", level: "WARN", message: "Connection degraded" },
    { timestamp: "2025-12-17T09:05:00Z", level: "INFO", message: "Recovered" },
  ]);

// Transform: Parse timestamp
const parseTimestamp = (entry: RawLogEntry): RawLogEntry => ({
  ...entry,
  timestamp: entry.timestamp, // Already ISO, but could parse here
});

// Transform: Map log level to severity
const mapSeverity = (level: string): "low" | "medium" | "high" => {
  if (level === "DEBUG" || level === "INFO") return "low";
  if (level === "WARN") return "medium";
  return "high";
};

// Transform: Normalize message
const normalizeMessage = (message: string): string =>
  message.toLowerCase().trim();

// Filter: Keep only important logs
const isImportant = (entry: RawLogEntry): boolean => {
  return entry.level !== "DEBUG";
};

// Main pipeline
const program = Effect.gen(function* () {
  console.log(`\n[STREAM] Processing log stream with map/filter\n`);

  // Create and transform stream
  const transformedStream = createLogStream().pipe(
    // Filter: Keep only non-debug logs
    Stream.filter((entry) => {
      const important = isImportant(entry);
      console.log(
        `[FILTER] ${entry.level} → ${important ? "✓ kept" : "✗ filtered out"}`
      );
      return important;
    }),

    // Map: Extract date
    Stream.map((entry) => {
      const date = new Date(entry.timestamp);
      console.log(`[MAP-1] Parsed date: ${date.toISOString()}`);
      return { ...entry, parsedDate: date };
    }),

    // Map: Normalize and map severity
    Stream.map((entry) => {
      const processed: ProcessedLog = {
        date: entry.parsedDate,
        severity: mapSeverity(entry.level),
        normalizedMessage: normalizeMessage(entry.message),
      };
      console.log(
        `[MAP-2] Transformed: ${entry.level} → ${processed.severity}`
      );
      return processed;
    })
  );

  // Collect all transformed logs
  const results = yield* transformedStream.pipe(
    Stream.runCollect
  );

  console.log(`\n[RESULTS]`);
  console.log(`  Total logs: ${results.length}`);

  Chunk.forEach(results, (log) => {
    console.log(
      `  - [${log.severity.toUpperCase()}] ${log.date.toISOString()}: ${log.normalizedMessage}`
    );
  });
});

Effect.runPromise(program);