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Merge branch 'hotfix' of https://github.com/ClusterCockpit/cc-backend into hotfix
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5743f4fa2a
@ -41,8 +41,9 @@ versions of third party packages.
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## Demo Setup
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We provide a shell skript that downloads demo data and automatically builds and starts cc-backend.
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You need `wget`, `go`, `node`, `rollup` and `yarn` in your path to start the demo. The demo will download 32MB of data (223MB on disk).
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We provide a shell skript that downloads demo data and automatically builds and
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starts cc-backend. You need `wget`, `go`, `node`, `npm` in your path to start
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the demo. The demo will download 32MB of data (223MB on disk).
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```sh
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git clone https://github.com/ClusterCockpit/cc-backend.git
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@ -0,0 +1,78 @@
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The job archive specifies an exchange format for job meta and performance metric
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data. It consists of two parts:
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* a [SQLite database schema](https://github.com/ClusterCockpit/cc-backend/wiki/Job-Archive#sqlite-database-schema) for job meta data and performance statistics
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* a [Json file format](https://github.com/ClusterCockpit/cc-backend/wiki/Job-Archive#json-file-format) together with a [Directory hierarchy specification](https://github.com/ClusterCockpit/cc-backend/wiki/Job-Archive#directory-hierarchy-specification)
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By using an open, portable and simple specification based on files it is
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possible to exchange job performance data for research and analysis purposes as
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well as use it as a robust way for archiving job performance data to disk.
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# SQLite database schema
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## Introduction
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A SQLite 3 database schema is provided to standardize the job meta data
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information in a portable way. The schema also includes optional columns for job
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performance statistics (called a job performance footprint). The database acts
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as a front end to filter and select subsets of job IDs, that are the keys to get
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the full job performance data in the job performance tree hierarchy.
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## Database schema
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The schema includes 3 tables: the job table, a tag table and a jobtag table
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representing the MANY-TO-MANY relation between jobs and tags. The SQL schema is
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specified
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[here](https://github.com/ClusterCockpit/cc-specifications/blob/master/schemas/jobs-sqlite.sql).
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Explanation of the various columns including the JSON datatypes is documented
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[here](https://github.com/ClusterCockpit/cc-specifications/blob/master/datastructures/job-meta.schema.json).
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# Directory hierarchy specification
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## Specification
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To manage the number of directories within a single directory a tree approach is
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used splitting the integer job ID. The job id is split in junks of 1000 each.
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Usually 2 layers of directories is sufficient but the concept can be used for an
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arbitrary number of layers.
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For a 2 layer schema this can be achieved with (code example in Perl):
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``` perl
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$level1 = $jobID/1000;
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$level2 = $jobID%1000;
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$dstPath = sprintf("%s/%s/%d/%03d", $trunk, $destdir, $level1, $level2);
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```
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## Example
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For the job ID 1034871 the directory path is `./1034/871/`.
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# Json file format
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## Overview
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Every cluster must be configured in a `cluster.json` file.
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The job data consists of two files:
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* `meta.json`: Contains job meta information and job statistics.
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* `data.json`: Contains complete job data with time series
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The description of the json format specification is available as [[json
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schema|https://json-schema.org/]] format file. The latest version of the json
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schema is part of the `cc-backend` source tree. For external reference it is
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also available in a separate repository.
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## Specification `cluster.json`
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The json schema specification is available
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[here](https://github.com/ClusterCockpit/cc-specifications/blob/master/datastructures/cluster.schema.json).
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## Specification `meta.json`
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The json schema specification is available
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[here](https://github.com/RRZE-HPC/HPCJobDatabase/blob/master/json-schema/job-meta.schema.json).
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## Specification `data.json`
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The json schema specification is available
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[here](https://github.com/RRZE-HPC/HPCJobDatabase/blob/master/json-schema/job-data.schema.json).
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Metric time series data is stored for a fixed time step. The time step is set
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per metric. If no value is available for a metric time series data timestamp
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`null` is entered.
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@ -363,6 +363,7 @@ func (fsa *FsArchive) CompressLast(starttime int64) int64 {
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b, err := os.ReadFile(filename)
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if err != nil {
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log.Errorf("fsBackend Compress - %v", err)
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os.WriteFile(filename, []byte(fmt.Sprintf("%d", starttime)), 0644)
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return starttime
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}
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last, err := strconv.ParseInt(strings.TrimSuffix(string(b), "\n"), 10, 64)
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