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Troubleshooting

This page describes troubleshooting steps for Billing Data Server (BDS).

Log

You can easily monitor the BDS application is by viewing log files on the Docker host. All logs are located under the ./cloudbilling-prem.local/log/ directory. The figure Sample main log file provides an example. Sample main log fine

There are three kind of logs:

  • Bds.log — the main log file, which contains log-records of daily BDS runs.
  • bds_stats.log — captures records with statistical information in key=value format.
  • brsctl.log, brs_config_snapshotter.log, db_utils.log, control_validation.log, premise_loader.log, and sbc_brs_comparator.log — captures log records of bds-utilities that are run manually.
  • bds-audit.log — captures records with a severity level of AUDIT (Available only in releases 9.0.000.1x and later).

The log file format is:

Date Time, Log Level, Thread ID | Module Name, Function Name - <Processing date, Tenant_id, Tenant name> Message

The possible log levels are:

  • AUDIT (Available in releases 9.0.000.18 and later)
  • CRITICAL
  • ERROR
  • WARNING
  • INFO
  • DEBUG

Genesys recommends that you monitor the logs for CRITICAL and ERROR level messages.

File Storage

BDS stores extracted and transformed data locally, in directories defined by the following variables in the gvars.py file:

  • local_cache
  • premise_extract_path
  • premise_transform_path

Directory structure for extracted data

Extracted data is stored locally within the directory identified by the “./local_cache/premise_extract_path” parameter, in the gvars.py file. Data within that directory is stored in subdirectories, as follows:

  • For Genesys Info Mart data sets, each data set produces one CSV file, each day, in subdirectories with the following naming:
    /<tenant_id>/<dataset_name>/<year>/<month> (MM)/<date_label>.csv.gz
  • For GVP Call Detail Record (CDR), there ais a seperate file for each location, in subdirectories with the following naming:
    /<tenant_id>/<dataset_name> (gvp_cdrs)/<region>/<location>/<year>/<month> (MM)/<day> (DD)/<date_label>.csv.gz

Directory structure for transformed data

Transformed data is stored locally within the directory identified by the “./local_cache/premise_transform_path” parameter in the gvars.py file. Data within that directory is stored in subdirectories, as follows:

  • For region-aware metrics:
    • Summary file:
      CNT_<US | EU | ...>_PES_<METRIC_NAME>_<GARNCODE_tenantID>_<datetime_with_timestamp>.CSV
    • Data file:
      <US | EU | ...>_PES_<METRIC_NAME>_<GARNCODE_tenantID>_<datetime_with_timestamp>.CSV
  • For global metrics:
    • Summary file:
      CNT_PES_<METRIC_NAME>_<GARNCODE_tenantID>_<datetime_with_timestamp>.CSV
    • Data file:
      PES_<METRIC_NAME>_<GARNCODE_tenantID>_<datetime_with_timestamp>.CSV

Timestamp encoding varies as follows:

  • Non-concurrent case : In filename_base, timestamp is 000000.
  • Concurrent case : In filename_base, timestamp is 000001 (plus one second).

For example:

  • US_PES_ASR_PORT_1000_2016_10_20T000000Z.CSV
    Meaning: US region, premise False, gvp_asr_ports metric, ID (must be more complex, unique for each tenant), time with 000000 timestamp - (not concurrent)
  • US_PES_AGENT_COBROWSE_1000_2016_10_11T000001Z.CSV
    Meaning: US region, premise False, seats_cobrowse metric, ID, time with 000001 timestamp - (concurrent)
This page was last modified on January 22, 2019, at 18:32.

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