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数据读取带键的Kafka记录：高效处理真实数据
12/21
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读取带键的Kafka记录是Apache Flink的一个重要功能。Flink提供了高效的读取器和同步读写机制，能够轻松处理异步消息源。以下是实现高效读取带键Kafka记录的具体步骤：
1. 读取Kafka记录
在Flink中，我们可以使用NewTableRead或NewMapReduceRead来读取Kafka记录。以下使用NewTableRead来实现：
flink
import org.apache.flink.data.table.TableRead;
import org.apache.flink.data.table.Column;
public class KafkaRead {
private final String keyColumn;
private final String titleColumn;
private final String descriptionColumn;
private final String filePath;
public KafkaRead(String keyColumn, String titleColumn, String descriptionColumn, String filePath) {
this.keyColumn = keyColumn;
this.titleColumn = titleColumn;
this.descriptionColumn = descriptionColumn;
this.filePath = filePath;
}
@Override
public Table read() {
return Table.read(
input = new TableRead(
keyColumn = keyColumn,
titleColumn = titleColumn,
descriptionColumn = descriptionColumn,
filePath = filePath,
lastLine = null
),
map = true
);
}
}
2. 处理键值对
在读取Kafka记录后，我们可以将键值对结构进行处理。假设我们希望将标题、关键词和描述提取出来，我们可以将键读取为标题，值读取为关键词和描述。
flink
import org.apache.flink.data.table.Column;
public KafkaRead(String keyColumn, String titleColumn, String descriptionColumn, String filePath) {
private final String keys = keyColumn;
private final String titleColumn = titleColumn;
private final String descriptionColumn = descriptionColumn;
private final String filePath;
public KafkaRead(String keyColumn, String titleColumn, String descriptionColumn, String filePath) {
this.keys = keyColumn;
this.titleColumn = titleColumn;
this.descriptionColumn = descriptionColumn;
this.filePath = filePath;
}
@Override
public Table read() {
return Table.read(
input = new TableRead(
keyColumn = keys,
titleColumn = titleColumn,
descriptionColumn = descriptionColumn,
filePath = filePath,
lastLine = null
),
map = true
);
}
}
3. 转换数据
接下来，我们可以将处理好的键值对结构转换为字典或向量。例如，将标题、关键词和描述转换为字典：
flink
import org.apache.flink.data.table.Column;
public KafkaRead(String keyColumn, String titleColumn, String descriptionColumn, String filePath) {
private final String keys = keyColumn;
private final String titleColumn = titleColumn;
private final String descriptionColumn = descriptionColumn;
private final String filePath;
public KafkaRead(String keyColumn, String titleColumn, String descriptionColumn, String filePath) {
this.keys = keyColumn;
this.titleColumn = titleColumn;
this.descriptionColumn = descriptionColumn;
this.filePath = filePath;
}
@Override
public Table read() {
return Table.read(
input = new TableRead(
keyColumn = keys,
titleColumn = titleColumn,
descriptionColumn = descriptionColumn,
filePath = filePath,
lastLine = null
),
map = true
);
}
@Override
public Collection convert() {
Collection<Dictionary<String, String>> dictCollection = new Collection<Dictionary<String, String>>();
return dictCollection.from(reading.read().map((Row row) -> {
return new Dictionary<String, String>() {
private key = row.get(keyColumn);
private value = row.get(titleColumn);
private description = row.get(descriptionColumn);
return new Value(value, description);
});
}).collect();
}
}
4. 分片数据
为了提高读取效率，我们可以将数据分片处理。Flink提供了分片功能，可以将数据按时间或键进行分片。
flink
import org.apache.flink.data.table.Row;
import org.apache.flink.data.table.DataFrame;
import org.apache.flink.data.table.GroupBy;
import org.apache.flink.data.table.Phi;
public KafkaRead(String keyColumn, String titleColumn, String descriptionColumn, String filePath) {
private final String keys = keyColumn;
private final String titleColumn = titleColumn;
private final String descriptionColumn = descriptionColumn;
private final String filePath;
public KafkaRead(String keyColumn, String titleColumn, String descriptionColumn, String filePath) {
this.keys = keyColumn;
this.titleColumn = titleColumn;
this.descriptionColumn = descriptionColumn;
this.filePath = filePath;
}
@Override
public Table read() {
return Table.read(
input = new TableRead(
keyColumn = keys,
titleColumn = titleColumn,
descriptionColumn = descriptionColumn,
filePath = filePath,
lastLine = null
),
map = true
);
}
@Override
public Collection convert() {
Collection<Phi<Dictionary<String, String>>> phiCollection = new Collection<Phi<Dictionary<String, String>>>();
return phiCollection.from(reading.read().map((Row row) -> {
Phi<Dictionary<String, String>> phi = new Phi<Dictionary<String, String>>();
phi.set("title", row.get(titleColumn));
phi.set("description", row.get(descriptionColumn));
phiCollection.add(phi);
}).collect();
}
}
5. 输出数据
最后，我们可以将处理好的数据以指定的格式输出。假设我们希望将数据以字典格式输出，我们可以将读取器设置为NewPhi。
flink
import org.apache.flink.data.table.Phi;
public KafkaRead(String keyColumn, String titleColumn, String descriptionColumn, String filePath) {
private final String keys = keyColumn;
private final String titleColumn = titleColumn;
private final String descriptionColumn = descriptionColumn;
private final String filePath;
public KafkaRead(String keyColumn, String titleColumn, String descriptionColumn, String filePath) {
this.keys = keyColumn;
this.titleColumn = titleColumn;
this.descriptionColumn = descriptionColumn;
this.filePath = filePath;
}
@Override
public Table read() {
return Table.read(
input = new TableRead(
keyColumn = keys,
titleColumn = titleColumn,
descriptionColumn = descriptionColumn,
filePath = filePath,
lastLine = null
),
map = true
);
}
@Override
public Collection convert() {
Collection<Phi<Dictionary<String, String>>> phiCollection = new Collection<Phi<Dictionary<String, String>>>();
return phiCollection.from(reading.read().map((Row row) -> {
Phi<Dictionary<String, String>> phi = new Phi<Dictionary<String, String>>();
phi.set("title", row.get(titleColumn));
phi.set("description", row.get(descriptionColumn));
phiCollection.add(phi);
}).collect();
}
}
结论：
通过以上步骤，我们可以高效读取带键的Kafka记录，并将其转换为所需的格式。Flink的高效读取器和同步读写机制，使得这一过程变得非常高效。此外，我们还可以通过分片和缓存机制进一步优化读取性能，确保在处理大量数据时仍然保持低吞吐量。
