SreeramGarlapati commented on a change in pull request #2660: URL: https://github.com/apache/iceberg/pull/2660#discussion_r652323493
########## File path: spark3/src/main/java/org/apache/iceberg/spark/source/SparkMicroBatchStream.java ########## @@ -0,0 +1,314 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +package org.apache.iceberg.spark.source; + +import java.io.BufferedWriter; +import java.io.IOException; +import java.io.InputStream; +import java.io.OutputStream; +import java.io.OutputStreamWriter; +import java.nio.charset.StandardCharsets; +import java.util.ArrayList; +import java.util.List; +import org.apache.hadoop.fs.Path; +import org.apache.iceberg.CombinedScanTask; +import org.apache.iceberg.DataOperations; +import org.apache.iceberg.FileScanTask; +import org.apache.iceberg.MicroBatches; +import org.apache.iceberg.MicroBatches.MicroBatch; +import org.apache.iceberg.Schema; +import org.apache.iceberg.SchemaParser; +import org.apache.iceberg.SerializableTable; +import org.apache.iceberg.Snapshot; +import org.apache.iceberg.SnapshotSummary; +import org.apache.iceberg.Table; +import org.apache.iceberg.io.CloseableIterable; +import org.apache.iceberg.io.FileIO; +import org.apache.iceberg.io.InputFile; +import org.apache.iceberg.io.OutputFile; +import org.apache.iceberg.relocated.com.google.common.base.Preconditions; +import org.apache.iceberg.relocated.com.google.common.collect.Lists; +import org.apache.iceberg.spark.Spark3Util; +import org.apache.iceberg.spark.SparkReadOptions; +import org.apache.iceberg.spark.source.SparkBatchScan.ReadTask; +import org.apache.iceberg.spark.source.SparkBatchScan.ReaderFactory; +import org.apache.iceberg.util.PropertyUtil; +import org.apache.iceberg.util.SnapshotUtil; +import org.apache.iceberg.util.TableScanUtil; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.broadcast.Broadcast; +import org.apache.spark.sql.connector.read.InputPartition; +import org.apache.spark.sql.connector.read.PartitionReaderFactory; +import org.apache.spark.sql.connector.read.streaming.MicroBatchStream; +import org.apache.spark.sql.connector.read.streaming.Offset; +import org.apache.spark.sql.util.CaseInsensitiveStringMap; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +import static org.apache.iceberg.TableProperties.SPLIT_LOOKBACK; +import static org.apache.iceberg.TableProperties.SPLIT_LOOKBACK_DEFAULT; +import static org.apache.iceberg.TableProperties.SPLIT_OPEN_FILE_COST; +import static org.apache.iceberg.TableProperties.SPLIT_OPEN_FILE_COST_DEFAULT; +import static org.apache.iceberg.TableProperties.SPLIT_SIZE; +import static org.apache.iceberg.TableProperties.SPLIT_SIZE_DEFAULT; + +public class SparkMicroBatchStream implements MicroBatchStream { + private static final Logger LOG = LoggerFactory.getLogger(SparkMicroBatchStream.class); + + private final JavaSparkContext sparkContext; + private final Table table; + private final boolean caseSensitive; + private final Schema expectedSchema; + private final Long splitSize; + private final Integer splitLookback; + private final Long splitOpenFileCost; + private final boolean localityPreferred; + private final StreamingOffset initialOffset; + + SparkMicroBatchStream(JavaSparkContext sparkContext, + Table table, boolean caseSensitive, Schema expectedSchema, + CaseInsensitiveStringMap options, String checkpointLocation) { + this.sparkContext = sparkContext; + this.table = table; + this.caseSensitive = caseSensitive; + this.expectedSchema = expectedSchema; + this.localityPreferred = Spark3Util.isLocalityEnabled(table.io(), table.location(), options); + + long tableSplitSize = PropertyUtil.propertyAsLong(table.properties(), SPLIT_SIZE, SPLIT_SIZE_DEFAULT); + this.splitSize = Spark3Util.propertyAsLong(options, SparkReadOptions.SPLIT_SIZE, tableSplitSize); + + int tableSplitLookback = PropertyUtil.propertyAsInt(table.properties(), SPLIT_LOOKBACK, SPLIT_LOOKBACK_DEFAULT); + this.splitLookback = Spark3Util.propertyAsInt(options, SparkReadOptions.LOOKBACK, tableSplitLookback); + + long tableSplitOpenFileCost = PropertyUtil.propertyAsLong( + table.properties(), SPLIT_OPEN_FILE_COST, SPLIT_OPEN_FILE_COST_DEFAULT); + this.splitOpenFileCost = Spark3Util.propertyAsLong(options, SPLIT_OPEN_FILE_COST, tableSplitOpenFileCost); + + InitialOffsetStore initialOffsetStore = InitialOffsetStore.getInstance(table, checkpointLocation); + this.initialOffset = getOrWriteInitialOffset(initialOffsetStore); + } + + @Override + public Offset latestOffset() { Review comment: That was my initial approach. But then, there is an issue with correctness - in the case of `Trigger.Once`. Seemslike [there is precedence ](https://databricks.com/blog/2017/05/22/running-streaming-jobs-day-10x-cost-savings.html) in using `Trigger.Once` in the context of automating Batch processing - where `readStream.Trigger(Trigger.Once)` is expected to return all available events. Due to the above reason - we cannot incrementally emitting the `latestOffsets` - unless the user **EXPLICITLY** expresses intent to return less amount of data than what is present in the table - for ex: via an option like `size`. & then, evaluating the concern that @aokolnychyi's & you (@rdblue's) raised; one particular place that I empathised is - ppl will try this new Streaming on their existing tables - which in most of the usecases - will be very large tables & will run into the problem of Spark cluster not being able to handle their first micro_batch. bad experience! After sleeping on this - I conclude that - **implementing rate limiting in the PR is the best way forward**. Pl. let me know your thoughts. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@iceberg.apache.org For additional commands, e-mail: issues-h...@iceberg.apache.org
