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Convert Rust books to EPUB (incl. The Rust Programming Language)

The following steps work for all the HTML learning materials on the Learn Rust page:

1. Click on the "Print this book" icon in the top right corner.
2. "Cancel" print popup.
3. Press F12 (on Firefox) to open the browser console. Or: Tools > Browser Tools > Web Developer Tools, then switch to Console tab.
4. Copy, paste, and run the contents of ebookFormatPreparation.js into your browser's console.
5. Save the modified HTML file.

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func isInputFromStdin() bool {
	info, err := os.Stdin.Stat()
	if err != nil {
		log.Fatal(err)
	}
	if info.Mode()&os.ModeNamedPipe == 0 {
		return false
	} else {
		return true
	}

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physics/shield/bullet_hit_shield_07.wav
physics/shield/bullet_hit_shield_06.wav
physics/shield/bullet_hit_shield_05.wav
physics/shield/bullet_hit_shield_04.wav
physics/shield/bullet_hit_shield_03.wav
physics/shield/bullet_hit_shield_02.wav
physics/shield/bullet_hit_shield_01.wav
player/winter/snowball_throw_04.wav
player/winter/snowball_throw_03.wav
player/winter/snowball_throw_02.wav

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CREATE STREAM products ...;

CREATE STREAM products_repartitioned
   WITH (PARTITIONS=42) AS
   SELECT * FROM products
   PARTITION BY product_id
   EMIT CHANGES;

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CREATE STREAM products ...;

CREATE STREAM products_repartitioned
   WITH (PARTITIONS=30) AS
   SELECT * FROM products
   EMIT CHANGES;

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// Create KTable from Kafka topic.
KTable<String, String> table = builder.table("input-topic", Consumed.with(Serdes.String(), Serdes.String()));

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-- Create ksqlDB table from Kafka topic.
CREATE TABLE myTable (username VARCHAR, location VARCHAR)
  WITH (KAFKA_TOPIC='input-topic', KEY='username', VALUE_FORMAT='...');

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-- Create ksqlDB stream from Kafka topic.
CREATE STREAM myStream (username VARCHAR, location VARCHAR)
  WITH (KAFKA_TOPIC='input-topic', VALUE_FORMAT='...');

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// Create KStream from Kafka topic.
StreamsBuilder builder = new StreamsBuilder();
KStream<String, String> stream =
  builder.stream("input-topic", Consumed.with(Serdes.String(), Serdes.String()));

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// Continuously aggregating a KStream into a KTable.
KStream<String, String> locationUpdatesStream = ...;

KTable<String, Long> locationsPerUser
   = locationUpdatesStream
       .groupBy((k, v) -> v.username)
       .count();