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https://issues.apache.org/jira/browse/STORM-960?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14646544#comment-14646544
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ASF GitHub Bot commented on STORM-960:
--------------------------------------
Github user harshach commented on a diff in the pull request:
https://github.com/apache/storm/pull/653#discussion_r35793063
--- Diff:
external/storm-hive/src/main/java/org/apache/storm/hive/bolt/HiveBolt.java ---
@@ -43,23 +43,25 @@
import java.util.concurrent.Executors;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.atomic.AtomicBoolean;
+import java.util.concurrent.BlockingQueue;
+import java.util.concurrent.LinkedBlockingQueue;
import java.io.IOException;
public class HiveBolt extends BaseRichBolt {
private static final Logger LOG =
LoggerFactory.getLogger(HiveBolt.class);
private OutputCollector collector;
private HiveOptions options;
- private Integer currentBatchSize;
private ExecutorService callTimeoutPool;
private transient Timer heartBeatTimer;
private Boolean kerberosEnabled = false;
private AtomicBoolean timeToSendHeartBeat = new AtomicBoolean(false);
private UserGroupInformation ugi = null;
HashMap<HiveEndPoint, HiveWriter> allWriters;
+ private BlockingQueue<Tuple> tupleBatch;
public HiveBolt(HiveOptions options) {
this.options = options;
- this.currentBatchSize = 0;
+ tupleBatch = new LinkedBlockingQueue<Tuple>();
--- End diff --
Bolt internals are not shared across the threads you can safely use List
> Hive-Bolt can lose tuples when flushing data
> --------------------------------------------
>
> Key: STORM-960
> URL: https://issues.apache.org/jira/browse/STORM-960
> Project: Apache Storm
> Issue Type: Improvement
> Components: external
> Reporter: Aaron Dossett
> Assignee: Aaron Dossett
> Priority: Minor
>
> In HiveBolt's execute method tuples are ack'd as they are received. When a
> batchsize of tuples has been received, the writers are flushed. However, if
> the flush fails only the most recent tuple will be marked as failed. All
> prior tuples will already have been ack'd. This creates a window for data
> loss.
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