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Access CX-Data Directly Access CX-Data Directly

Last Updated: Jan. 04, 2023

This guide explains how to query your S3 Coralogix archive bucket (cx-data) using a third-party framework with the standard Apache Parquet reader provided by the relevant framework and required schema.

Folder Structure

cx-data is stored in standard hive-like partitions, with the following partition fields:

  • team_id=<team-id>: Coralogix Team ID
  • dt=YYYY-MM-DD: Date of the data in UTC
  • hr=HH: Hour of the data in UTC

These fields can be defined as virtual columns inside the framework, serving as filters in a query.


  • Both dt and hr are based on the event timestamp.
  • The team_id=<team-id> partition allows reusing the same bucket and prefix to write data from multiple Coralogix teams and query them all in one query.


Each Apache Parquet file has three fields with data as JSON-formatted strings:

  • src_obj__event_metadata: JSON object containing metadata related to the event
  • src_obj__event_labels: JSON object containing the labels of the event (such as the Coralogix applicationName and subsystemName)
  • src_obj__user_data: JSON object containing actual event data


Below is an example of each of the 3 payload fields.


  "timestamp": "2022-03-28T08:50:57.946",
  "severity": "Debug",
  "priorityclass": "low",
  "logid": "some-uuid"


  "applicationname": "some-app",
  "subsystemname": "some-subsystem",
  "category": "some-category",
  "classname": "some-class",
  "methodname": "some-method",
  "computername": "some-computer",
  "threadid": "some-thread-id",
  "ipaddress": "some-ip-address"


  "_container_id": "0f099482cf3b507462020e9052516554b65865fb761af8e076735312772352bf",
  "host": "ip-10-1-11-144",
  "short_message": " - - [28/Mar/2022:08:50:57 +0000] \\"GET /check HTTP/1.1\\" 200 16559 \\"-\\" \\"Consul Health Check\\" \\"-\\""

Reading cx-data Files Using a Standard Framework


Loading cx-data files in Pandas can be done using the read_parquet method:

import pandas as pd

# Notice that only the three payload columns are passed eventually to read_parquet()

cx_columns = [

df = pd.read_parquet('s3://.../myfile.parquet',columns = cx_columns)

# The dataframe `df` contains all the data needed for further processing.

Here is the output of df.info() showing the expected schema of the DataFrame:


### Output

RangeIndex: 161050 entries, 0 to 161049
Data columns (total 3 columns):
 #   Column                   Non-Null Count   Dtype
---  ------                   --------------   -----
 0   src_obj__event_metadata  161050 non-null  object
 1   src_obj__event_labels    161050 non-null  object
 2   src_obj__user_data       161050 non-null  object
dtypes: object(3)


To use cx-data directly in Athena, you’ll need to create an EXTERNAL table as follows:

	`src_obj__event_labels` STRING,
	`src_obj__event_metadata` STRING,
	`src_obj__user_data` STRING,
PARTITIONED BY (`team_id` string, `dt` string, `hr` string)
ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
STORED AS INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
LOCATION 's3://<bucket>/parquet/v1/'

After creating the external table, table partitions should be added to limit the scope of queries, reducing costs and improving performance.

Table partitions can be added manually:

ALTER TABLE "my_table" ADD PARTITION (team_id='23333', dt='2022-05-30', hr='01');

# Additional ALTER TABLE ... ADD PARTITION statements according to which dates and hours are needed

Table partitions can also be added automatically:


Note! This command scans all of the files in the table location, which may be a time-consuming task when the amount of partitions detected is large. It may be easier to add the relevant new partitions manually. One option to expedite automatic partitioning is to limit the scope of the data by specifying a specific team_id and dt in the table definition LOCATION (e.g. modify the CREATE EXTERNAL TABLE command to include the team_id=<team-id>/dt=<date>/ subpath). This restricts the partition auto-detection to the relevant dates.

After adding the relevant partitions, a query can be executed on the table. Accessing a specific field inside one of the payload fields should be done using the Athena function json_extract_scalar, as in the following example:

  json_extract_scalar(src_obj__event_metadata,'$.severity') severity,
  json_extract_scalar(src_obj__event_labels,'$.applicationName') app_name,
  json_extract_scalar(src_obj__user_data,'$.my_obj.my_message_field') message_field
FROM "my_table"


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