| /* |
| * 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.asterix.metadata; |
| |
| import java.io.DataOutput; |
| import java.io.Serializable; |
| |
| import org.apache.asterix.common.config.DatasetConfig.DatasetType; |
| import org.apache.hyracks.api.exceptions.HyracksDataException; |
| |
| public interface IDatasetDetails extends Serializable { |
| |
| public DatasetType getDatasetType(); |
| |
| public void writeDatasetDetailsRecordType(DataOutput out) throws HyracksDataException; |
| |
| /** |
| * @return if the dataset is a temporary dataset. |
| * Here is a summary of temporary datasets: |
| * 1. Different from a persistent dataset, reads and writes over a temporary dataset do not require any lock. |
| * Writes over a temporary dataset do not generate any write-ahead update and commit log but generate |
| * flush log and job commit log. |
| * 2. A temporary dataset can only be an internal dataset, stored in partitioned LSM-Btrees. |
| * 3. All secondary indexes for persistent datasets are supported for temporary datasets. |
| * 4. A temporary dataset will be automatically garbage collected if it is not active in the past 30 days. |
| * A temporary dataset could be used for the following scenarios: |
| * 1. A data scientist wants to run some one-time data analysis queries over a dataset that s/he pre-processed |
| * and the dataset is only used by her/himself in an one-query-at-a-time manner. |
| * 2. Articulate AQL with external systems such as Pregelix/IMRU/Spark. A user can first run an AQL |
| * query to populate a temporary dataset, then kick off an external runtime to read this dataset, |
| * dump the results of the external runtime to yet-another-temporary dataset, and finally run yet-another AQL |
| * over the second temporary dataset. |
| */ |
| public boolean isTemp(); |
| |
| public long getLastAccessTime(); |
| } |