Alluxio
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AI-3.6 (stable)
AI-3.6 (stable)
  • Overview
    • Alluxio Namespace and Under File System
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    • Multi Tenancy and Unified Management
    • I/O Resiliency
  • Getting Started with K8s
    • Resource Prerequisites and Compatibility
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      • Install on Kubernetes
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    • Monitoring and Metrics
    • Management Console
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      • Navigation
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    • Cluster Administration
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  • Storage Integrations
    • Amazon AWS S3
    • Google Cloud GCS
    • Azure Blob Store
    • Aliyun OSS
    • Tencent COS
    • Volcengine TOS
    • Baidu Object Storage
    • HDFS
    • Network Attached Storage (NAS)
  • Data Access
    • Access via FUSE (POSIX API)
      • Client Writeback
      • Client Virtual Path Mapping
    • Access via S3 API
    • Access via PythonSDK/FSSpec
    • Data Access High Availability
      • Multiple Replicas
      • Multiple Availability Zones (AZ)
    • Performance Optimizations
      • File Reading
      • File Writing
      • Metadata Listing
    • UFS Bandwidth Limiter
  • Cache Management
    • Cache Filter Policy
    • Cache Loading
    • Cache Eviction
      • Manual Eviction by Free Command
      • Auto Eviction by TTL Policy
      • Auto Eviction by Priority Policy
    • Stale Cache Cleaning
    • Cache Quota
  • Performance Benchmarks
    • Fio (POSIX) Benchmark
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    • TLS Support
  • Reference
    • User CLI
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    • S3 API Usage
    • Third Party Licenses
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  • Usage
  • job load CLI
  • REST API
  1. Cache Management

Cache Loading

Last updated 6 days ago

Distributed load allows users to load data from UFS to Alluxio cluster efficiently. This can be used to initialize the Alluxio cluster to be able to immediately serve cached data when running workloads on top of Alluxio. For example, distributed load can be used to prefetch data for machine learning jobs, speeding up the training process. Distributed load can utilize and to enhance file distribution in scenarios with highly concurrent data access.

Usage

There are two recommended ways to trigger distributed load:

job load CLI

The job load command can be used to load data from UFS (Under File System) to the Alluxio cluster. The CLI sends a load request to the Alluxio coordinator, which subsequently distributes the load operation to all worker nodes.

bin/alluxio job load [flags] <path>

# Example output
Progress for loading path '/path':
        Settings:       bandwidth: unlimited    verify: false
        Job State: SUCCEEDED
        Files Processed: 1000
        Bytes Loaded: 125.00MB
        Throughput: 2509.80KB/s
        Block load failure rate: 0.00%
        Files Failed: 0

For detailed usage of CLI, please refer to the documentation.

REST API

Similar to the CLI, the REST API can also be used to load data. Requests are sent directly to the coordinator.

curl -H "Content-Type: application/json"  -v -X POST http://coordinator_host:19999/api/v1/master/submit_job/load -d '{
    "path": "s3://alluxiow/testm/dir-1/",
    "options": {
         "replicas":"2",
         "batchSize": "300",
         "partialListing": "true",
         "loadMetadataOnly": "true",
         "skipIfExists": "true"
    }
}'

Progress can be checked by sending a GET request with the same path.

curl -H "Content-Type: application/json"  -v -X GET http://coordinator_host:19999/api/v1/master/progress_job/load -d '{
  "path or indexFile": "s3://bucket/dir-1/",
  "format": "TEXT[default] | JSON",
  "verbose": "true"
}'

The load operation can be terminated by sending a POST request.

curl -H "Content-Type: application/json"  -v -X POST http://coordinator_host:19999/api/v1/master/stop_job/load -d '{
  "path || indexFile": "s3://alluxiow/testm/dir-1/"
}'

The load jobs can be list by sending a POST request. The results only include load tasks within seven days. The residence time of historical tasks can be configured through alluxio.job.retention.time.

curl http://ip:19999/api/v1/master/list_job?[job-type=LOAD[&job-state=[RUNNING|VERIFYING|STOPPED|SUCCEEDED|FAILED|ALL]]
file segmentation
multi-replication
job load