Examples with SparkShellScriptRunner com.thinkbiganalytics.spark.rest.controller.SparkShellScriptRunner used on opensource projects

Example 1 with SparkShellScriptRunner

use of com.thinkbiganalytics.spark.rest.controller.SparkShellScriptRunner in project kylo by Teradata.

the class FileMetadataCompletionTask method run.

@Override
public void run() {
    List<String> exceptions = new ArrayList<>();
    // aggregrate
    if (this.tasks != null) {
        exceptions.addAll(this.tasks.stream().filter(task -> task.hasExecutionException()).map(task -> task.getExecutionException()).collect(Collectors.toList()));
    }
    try {
        for (Map.Entry<SparkShellScriptRunner, List<com.thinkbiganalytics.spark.rest.model.FileMetadataResponse.ParsedFileMetadata>> e : this.fileSchemaScriptRunnerMap.entrySet()) {
            SparkShellScriptRunner runner = e.getKey();
            TransformResponse response = runner.getFinalResponse();
            if (response != null) {
                TransformResponseUtil responseTransformer = new TransformResponseUtil(response);
                Map<String, FileMetadataSchema> schemaMap = response.getResults().getRows().stream().map(row -> {
                    String mimeType = (String) responseTransformer.getRowValue("mimeType", row);
                    String filePath = (String) responseTransformer.getRowValue("resource", row);
                    Map<String, String> schema = responseTransformer.getRowValue("schema", row, Map.class);
                    return new FileMetadataSchema(mimeType, filePath, schema);
                }).collect(Collectors.toMap(key -> key.getFilePath(), key -> key));
                e.getValue().stream().forEach(originalData -> {
                    FileMetadataSchema updatedSchema = schemaMap.get(originalData.getFilePath());
                    if (updatedSchema != null) {
                        String headers = ObjectMapperSerializer.serialize(updatedSchema.getSchema());
                        originalData.getProperties().put("headers", headers);
                        originalData.getProperties().put("headerCount", updatedSchema.getSchema().size() + "");
                    }
                });
            }
        }
    } catch (Exception e) {
        exceptions.add(e.getMessage());
    }
    if (!exceptions.isEmpty()) {
        String message = exceptions.stream().collect(Collectors.joining("\n"));
        this.modifiedTransformResponse.setMessage(message);
    }
    this.finalResult.complete(this.modifiedTransformResponse);
    this.complete = true;
}

Example 2 with SparkShellScriptRunner

use of com.thinkbiganalytics.spark.rest.controller.SparkShellScriptRunner in project kylo by Teradata.

the class FileMetadataTaskService method findFileMetadataSchemas.

/**
 * Group the files by their respective mime type
 * For each mime type that spark can process create a task to determine the header information
 */
public void findFileMetadataSchemas(ModifiedTransformResponse<FileMetadataResponse> modifiedTransformResponse, FileMetadataTransformResponseModifier resultModifier) {
    FileMetadataCompletionTask result = new FileMetadataCompletionTask(modifiedTransformResponse, resultModifier);
    metadataResultCache.put(result.getTableId(), result);
    Map<String, List<com.thinkbiganalytics.spark.rest.model.FileMetadataResponse.ParsedFileMetadata>> mimeTypeGroup = resultModifier.groupByMimeType();
    List<String> mimeTypes = Lists.newArrayList(mimeTypeGroup.keySet());
    mimeTypes.removeIf(type -> !PARSABLE_MIME_TYPES.contains(type));
    List<SparkShellScriptRunner> tasks = new ArrayList<>();
    for (String mimeType : mimeTypes) {
        List<com.thinkbiganalytics.spark.rest.model.FileMetadataResponse.ParsedFileMetadata> data = mimeTypeGroup.get(mimeType);
        if (mimeType == "application/xml") {
            // need to group by rowtag
            Map<String, List<com.thinkbiganalytics.spark.rest.model.FileMetadataResponse.ParsedFileMetadata>> rowTags = data.stream().collect(Collectors.groupingBy(row -> row.getRowTag()));
            for (Map.Entry<String, List<com.thinkbiganalytics.spark.rest.model.FileMetadataResponse.ParsedFileMetadata>> rows : rowTags.entrySet()) {
                List<String> files = rows.getValue().stream().map(r -> r.getFilePath()).collect(Collectors.toList());
                SparkShellScriptRunner shellScriptRunner = new SparkShellScriptRunner(sparkShellUserProcessService, restClient, getUsername(), FileMetadataSchemaScriptBuilder.getSparkScript(mimeType, rows.getKey(), files), mimeType);
                tasks.add(shellScriptRunner);
                result.addTask(shellScriptRunner, rows.getValue());
            }
        } else {
            List<String> files = data.stream().map(r -> r.getFilePath()).collect(Collectors.toList());
            SparkShellScriptRunner shellScriptRunner = new SparkShellScriptRunner(sparkShellUserProcessService, restClient, getUsername(), FileMetadataSchemaScriptBuilder.getSparkScript(mimeType, null, files), mimeType);
            tasks.add(shellScriptRunner);
            result.addTask(shellScriptRunner, data);
        }
    }
    submitTasks(result, tasks);
}