> ## Documentation Index > Fetch the complete documentation index at: https://doc.lucidworks.com/llms.txt > Use this file to discover all available pages before exploring further. # Get a job's last run > Fetch the state, configuration, and result of a job's most recent run. ## OpenAPI ````yaml /api-reference/5.9/fusion-api-job-launcher.json get /job-launcher/launcher/status/{id} openapi: 3.0.3 info: title: Job Launcher Service API description: Launch and control Spark jobs. contact: name: Lucidworks url: www.lucidworks.com email: support@lucidworks.com license: name: License of the API url: https://lucidworks.com/legal/developer-license-agreement/ version: '5.9' servers: - url: https://{FUSION_HOST} description: Fusion variables: FUSION_HOST: default: FUSION_HOST description: Your environment host. security: [] tags: - name: Job Management description: >- APIs to list, retrieve, and manage Spark jobs, including job history and metadata - name: Job Launching description: APIs to launch Spark jobs with specified configurations and parameters - name: Job Logs description: APIs to retrieve log output and diagnostic information from launched jobs - name: Job Status and Progress description: >- APIs to check the current state, progress, and result of running or completed jobs - name: Job Configuration description: >- APIs to define, modify, or retrieve job configurations and runtime parameters externalDocs: description: Lucidworks Documentation url: https://doc.lucidworks.com/ paths: /job-launcher/launcher/status/{id}: get: tags: - Job Status and Progress summary: Get a job's last run description: Fetch the state, configuration, and result of a job's most recent run. operationId: getJobStatus parameters: - name: id description: The name of the job. example: api-test-app_click_signals_aggregation in: path required: true schema: type: string responses: '200': description: OK content: application/hal+json: schema: $ref: '#/components/schemas/SparkJobStatus' components: schemas: SparkJobStatus: type: object properties: state: type: string enum: - unknown - idle - starting - running - finishing - cancelling - finished - cancelled - error - skipped example: finished jobId: type: string description: >- The job run ID. This is distinct from `id` which is the name of the job configuration. example: hhzitrwancwv jobConfig: $ref: '#/components/schemas/SparkJobConfig' hostname: type: string example: driver-api-test-app-click-signal-sjnamxwxsyhq result: $ref: '#/components/schemas/SparkJobResult' startTime: type: string example: '2025-10-16T19:49:15.106Z' endTime: type: string example: '2025-10-16T19:49:42.276Z' duration: type: integer format: int64 example: 27170 SparkJobConfig: type: object properties: id: type: string description: >- The name of the job configuration. This is distinct from the job run ID (found in job status responses), which identifies a specific job run. example: api-test-app_click_signals_aggregation sparkConfig: type: array description: >- The configuration for this Spark job. The available keys depend on the job type. You can use the `/spark/schema` endpoint to fetch the configuration schemas for all Spark job types. items: $ref: '#/components/schemas/KeyValue' type: type: string enum: - aggregation - als_recommender - argo-classification - argo-data-augmentation - argo-delete-model - argo-delete-ray-model - argo-deploy-model - argo-deploy-ray-model - argo-item-recommender-content - argo-item-recommender-user - argo-milvus-create-collections - argo-milvus-create-indexes - argo-milvus-delete-collections - argo-milvus-delete-indexes - argo-qna-coldstart - argo-qna-evaluate - argo-qna-supervised - argo-upload-model - build-training - cluster_labeling - custom_python_job - custom_spark_scala_job - doc_clustering - experiment_sql - ground_truth - headTailAnalysis - logistic_regression_classifier_trainer - outlier_detection - parallel-bulk-loader - query_similarity - random_forests_classifier - ranking_metrics - script - similar_queries - sip - sql_template - synonymDetection - tokenPhraseSpellCorrection - transfer - trending-recommender - word2vec description: The Spark job type. discriminator: propertyName: type example: type: aggregation id: api-test-app_click_signals_aggregation inputCollection: api-test-app_signals sourceCatchup: true sourceRemove: false sql: >- WITH sigs_with_filters AS ( SELECT c.query as query, c.doc_id, q.filters_s as filters, c.type, c.ref_time, coalesce(c.count_i,1) as count_i, c.timestamp_tdt, greatest(coalesce(c.weight_d,0.1),0.0) as weight_d FROM api-test-app_signals c LEFT JOIN (SELECT id, filters_s FROM api-test-app_signals WHERE type='response') q ON q.id = c.fusion_query_id WHERE c.type IN ('click','cart','purchase') AND c.timestamp_tdt >= c.catchup_timestamp_tdt ), signal_type_groups AS ( SELECT SUM(count_i) AS typed_aggr_count_i, query, doc_id, type, filters, time_decay(count_i, timestamp_tdt, "30 days", ref_time, weight_d) AS typed_weight_d FROM sigs_with_filters GROUP BY doc_id, query, filters, type ) SELECT concat_ws('|', query, doc_id, filters) as id, SUM(typed_aggr_count_i) AS aggr_count_i, query AS query_s, query AS query_t, doc_id AS doc_id_s, filters AS filters_s, SPLIT(filters, ' \\$ ') AS filters_ss, weighted_sum(typed_weight_d, type, 'click:1.0,cart:10.0,purchase:25.0') AS weight_d FROM signal_type_groups GROUP BY query, doc_id, filters rollupSql: |- SELECT concat_ws('|', query_s, doc_id_s, filters_s) as id, query_s, query_s as query_t, doc_id_s, filters_s, first(aggr_type_s) AS aggr_type_s, SPLIT(filters_s, ' \\$ ') AS filters_ss, SUM(weight_d) AS weight_d, SUM(aggr_count_i) AS aggr_count_i FROM api-test-app_signals_aggr GROUP BY query_s, doc_id_s, filters_s referenceTime: '2025-10-17T18:56:14.660Z' skipCheckEnabled: true readOptions: - key: splits_per_shard value: '4' skipJobIfSignalsEmpty: true parameters: - key: signalTypeWeights value: click:1.0,cart:10.0,purchase:25.0 - key: signalTypes value: _regex/signalTypeWeights/([\w\-\.]*):([\d\.\-]*)(,|$)/'$1'$3/g selectQuery: '*:*' outputCollection: api-test-app_signals_aggr useNaturalKey: true optimizeSegments: 0 dataFormat: solr sparkSQL: SELECT * from spark_input sparkPartitions: 200 SparkJobResult: type: object properties: jobConfigId: type: string jobRunId: type: string exception: type: object additionalProperties: type: object example: jobConfigId: api-test-app_click_signals_aggregation jobRunId: sjnamxwxsyhq aggrClass: SQL query: >- WITH sigs_with_filters AS (SELECT c.query as query, c.doc_id, q.filters_s as filters, c.type, c.ref_time, coalesce(c.count_i,1) as count_i, c.timestamp_tdt, greatest(coalesce(c.weight_d,0.1),0.0) as weight_d FROM api_test_app_signals c LEFT JOIN (SELECT id, filters_s FROM api_test_app_signals WHERE type='response') q ON q.id = c.fusion_query_id WHERE c.type IN ('click','cart','purchase') AND c.timestamp_tdt >= c.catchup_timestamp_tdt ), signal_type_groups AS (SELECT SUM(count_i) AS typed_aggr_count_i, query, doc_id, type, filters, time_decay(count_i, timestamp_tdt, "30 days", ref_time, weight_d) AS typed_weight_d FROM sigs_with_filters GROUP BY doc_id, query, filters, type) SELECT concat_ws('|', query, doc_id, filters) as id, SUM(typed_aggr_count_i) AS aggr_count_i, query AS query_s, query AS query_t, doc_id AS doc_id_s, filters AS filters_s, SPLIT(filters, ' \\$ ') AS filters_ss, weighted_sum(typed_weight_d, type, 'click:1.0,cart:10.0,purchase:25.0') AS weight_d FROM signal_type_groups GROUP BY query, doc_id, filters state: finished aggregated: 0 applicationId: spark-0424dda9215441c0b232210af1d7cb67 podId: driver-api-test-app-click-signal-sjnamxwxsyhq aggr_type_s: click@doc_id,filters,query KeyValue: type: object properties: key: type: string value: type: string ````