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S3 Data Lake

Incubating

Important Capabilities​

CapabilityStatusNotes
Data Profiling✅Optionally enabled via configuration
Extract Tags✅Can extract S3 object/bucket tags if enabled

This plugin extracts:

  • Row and column counts for each table
  • For each column, if profiling is enabled:
    • null counts and proportions
    • distinct counts and proportions
    • minimum, maximum, mean, median, standard deviation, some quantile values
    • histograms or frequencies of unique values

This connector supports both local files as well as those stored on AWS S3 (which must be identified using the prefix s3://). Supported file types are as follows:

  • CSV
  • TSV
  • JSON
  • Parquet
  • Apache Avro

Schemas for Parquet and Avro files are extracted as provided.

Schemas for schemaless formats (CSV, TSV, JSON) are inferred. For CSV and TSV files, we consider the first 100 rows by default, which can be controlled via the max_rows recipe parameter (see below) JSON file schemas are inferred on the basis of the entire file (given the difficulty in extracting only the first few objects of the file), which may impact performance. We are working on using iterator-based JSON parsers to avoid reading in the entire JSON object.

Note that because the profiling is run with PySpark, we require Spark 3.0.3 with Hadoop 3.2 to be installed (see compatibility for more details). If profiling, make sure that permissions for s3a:// access are set because Spark and Hadoop use the s3a:// protocol to interface with AWS (schema inference outside of profiling requires s3:// access). Enabling profiling will slow down ingestion runs.

CLI based Ingestion​

Install the Plugin​

pip install 'acryl-datahub[s3]'

Starter Recipe​

Check out the following recipe to get started with ingestion! See below for full configuration options.

For general pointers on writing and running a recipe, see our main recipe guide.

source:
  type: s3
  config:
    path_specs:
      -
        include: "s3://covid19-lake/covid_knowledge_graph/csv/nodes/*.*"

    aws_config:
      aws_access_key_id: *****
      aws_secret_access_key: *****
      aws_region: us-east-2
    env: "PROD"
    profiling:
      enabled: false

# sink configs

Config Details​

Note that a . is used to denote nested fields in the YAML recipe.

FieldDescription
max_rows
integer
Maximum number of rows to use when inferring schemas for TSV and CSV files.
Default: 100
platform
string
The platform that this source connects to (either 's3' or 'file'). If not specified, the platform will be inferred from the path_specs.
Default:
platform_instance
string
The instance of the platform that all assets produced by this recipe belong to
spark_driver_memory
string
Max amount of memory to grant Spark.
Default: 4g
update_schema_on_partition_file_updates
boolean
Whether to update the table schema when schema in files within the partitions are updated.
Default: False
use_s3_bucket_tags
boolean
Whether or not to create tags in datahub from the s3 bucket
use_s3_object_tags
boolean
# Whether or not to create tags in datahub from the s3 object
verify_ssl
One of boolean, string
Either a boolean, in which case it controls whether we verify the server's TLS certificate, or a string, in which case it must be a path to a CA bundle to use.
Default: True
env
string
The environment that all assets produced by this connector belong to
Default: PROD
aws_config
AwsConnectionConfig
AWS configuration
aws_config.aws_region ❓
string
AWS region code.
aws_config.aws_access_key_id
string
AWS access key ID. Can be auto-detected, see https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html for details.
aws_config.aws_endpoint_url
string
Autodetected. See https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html
aws_config.aws_profile
string
Named AWS profile to use. Only used if access key / secret are unset. If not set the default will be used
aws_config.aws_proxy
map(str,string)
aws_config.aws_secret_access_key
string
AWS secret access key. Can be auto-detected, see https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html for details.
aws_config.aws_session_token
string
AWS session token. Can be auto-detected, see https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html for details.
aws_config.aws_role
One of string, union(anyOf), string, AwsAssumeRoleConfig
AWS roles to assume. If using the string format, the role ARN can be specified directly. If using the object format, the role can be specified in the RoleArn field and additional available arguments are documented at https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts.html?highlight=assume_role#STS.Client.assume_role
aws_config.aws_role.RoleArn ❓
string
ARN of the role to assume.
aws_config.aws_role.ExternalId
string
External ID to use when assuming the role.
path_specs
array(object)
path_specs.include ❓
string
Path to table (s3 or local file system). Name variable {table} is used to mark the folder with dataset. In absence of {table}, file level dataset will be created. Check below examples for more details.
path_specs.default_extension
string
For files without extension it will assume the specified file type. If it is not set the files without extensions will be skipped.
path_specs.enable_compression
boolean
Enable or disable processing compressed files. Currently .gz and .bz files are supported.
Default: True
path_specs.exclude
array(string)
path_specs.file_types
array(string)
path_specs.sample_files
boolean
Not listing all the files but only taking a handful amount of sample file to infer the schema. File count and file size calculation will be disabled. This can affect performance significantly if enabled
Default: True
path_specs.table_name
string
Display name of the dataset.Combination of named variables from include path and strings
profile_patterns
AllowDenyPattern
regex patterns for tables to profile
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
profile_patterns.allow
array(string)
profile_patterns.deny
array(string)
profile_patterns.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
profiling
DataLakeProfilerConfig
Data profiling configuration
Default: {'enabled': False, 'profile_table_level_only': Fal...
profiling.enabled
boolean
Whether profiling should be done.
Default: False
profiling.include_field_distinct_value_frequencies
boolean
Whether to profile for distinct value frequencies.
Default: True
profiling.include_field_histogram
boolean
Whether to profile for the histogram for numeric fields.
Default: True
profiling.include_field_max_value
boolean
Whether to profile for the max value of numeric columns.
Default: True
profiling.include_field_mean_value
boolean
Whether to profile for the mean value of numeric columns.
Default: True
profiling.include_field_median_value
boolean
Whether to profile for the median value of numeric columns.
Default: True
profiling.include_field_min_value
boolean
Whether to profile for the min value of numeric columns.
Default: True
profiling.include_field_null_count
boolean
Whether to profile for the number of nulls for each column.
Default: True
profiling.include_field_quantiles
boolean
Whether to profile for the quantiles of numeric columns.
Default: True
profiling.include_field_sample_values
boolean
Whether to profile for the sample values for all columns.
Default: True
profiling.include_field_stddev_value
boolean
Whether to profile for the standard deviation of numeric columns.
Default: True
profiling.max_number_of_fields_to_profile
integer
A positive integer that specifies the maximum number of columns to profile for any table. None implies all columns. The cost of profiling goes up significantly as the number of columns to profile goes up.
profiling.profile_table_level_only
boolean
Whether to perform profiling at table-level only or include column-level profiling as well.
Default: False

Path Specs​

Example - Dataset per file

Bucket structure:

test-s3-bucket
├── employees.csv
└── food_items.csv

Path specs config

path_specs:
    - include: s3://test-s3-bucket/*.csv

Example - Datasets with partitions

Bucket structure:

test-s3-bucket
├── orders
│   └── year=2022
│       └── month=2
│           ├── 1.parquet
│           └── 2.parquet
└── returns
    └── year=2021
        └── month=2
            └── 1.parquet

Path specs config:

path_specs:
    - include: s3://test-s3-bucket/{table}/{partition_key[0]}={partition[0]}/{partition_key[1]}={partition[1]}/*.parquet

Example - Datasets with partition and exclude

Bucket structure:

test-s3-bucket
├── orders
│   └── year=2022
│       └── month=2
│           ├── 1.parquet
│           └── 2.parquet
└── tmp_orders
    └── year=2021
        └── month=2
            └── 1.parquet


Path specs config:

path_specs:
    - include: s3://test-s3-bucket/{table}/{partition_key[0]}={partition[0]}/{partition_key[1]}={partition[1]}/*.parquet
      exclude: 
        - **/tmp_orders/**

Example - Datasets of mixed nature

Bucket structure:

test-s3-bucket
├── customers
│   ├── part1.json
│   ├── part2.json
│   ├── part3.json
│   └── part4.json
├── employees.csv
├── food_items.csv
├── tmp_10101000.csv
└──  orders
    └── year=2022
        └── month=2
            ├── 1.parquet
            ├── 2.parquet
            └── 3.parquet

Path specs config:

path_specs:
    - include: s3://test-s3-bucket/*.csv
      exclude:
        - **/tmp_10101000.csv
    - include: s3://test-s3-bucket/{table}/*.json
    - include: s3://test-s3-bucket/{table}/{partition_key[0]}={partition[0]}/{partition_key[1]}={partition[1]}/*.parquet

Valid path_specs.include

s3://my-bucket/foo/tests/bar.avro # single file table   
s3://my-bucket/foo/tests/*.* # mulitple file level tables
s3://my-bucket/foo/tests/{table}/*.avro #table without partition
s3://my-bucket/foo/tests/{table}/*/*.avro #table where partitions are not specified
s3://my-bucket/foo/tests/{table}/*.* # table where no partitions as well as data type specified
s3://my-bucket/{dept}/tests/{table}/*.avro # specifying keywords to be used in display name
s3://my-bucket/{dept}/tests/{table}/{partition_key[0]}={partition[0]}/{partition_key[1]}={partition[1]}/*.avro # specify partition key and value format
s3://my-bucket/{dept}/tests/{table}/{partition[0]}/{partition[1]}/{partition[2]}/*.avro # specify partition value only format
s3://my-bucket/{dept}/tests/{table}/{partition[0]}/{partition[1]}/{partition[2]}/*.* # for all extensions
s3://my-bucket/*/{table}/{partition[0]}/{partition[1]}/{partition[2]}/*.* # table is present at 2 levels down in bucket
s3://my-bucket/*/*/{table}/{partition[0]}/{partition[1]}/{partition[2]}/*.* # table is present at 3 levels down in bucket

Valid path_specs.exclude

  • **/tests/**
  • s3://my-bucket/hr/**
  • */tests/.csv
  • s3://my-bucket/foo/*/my_table/**

Notes

  • {table} represents folder for which dataset will be created.
  • include path must end with (. or *.[ext]) to represent leaf level.
  • if *.[ext] is provided then only files with specified type will be scanned.
  • /*/ represents single folder.
  • {partition[i]} represents value of partition.
  • {partition_key[i]} represents name of the partition.
  • While extracting, “i” will be used to match partition_key to partition.
  • all folder levels need to be specified in include. Only exclude path can have ** like matching.
  • exclude path cannot have named variables ( {} ).
  • Folder names should not contain {, }, *, / in their names.
  • {folder} is reserved for internal working. please do not use in named variables.

If you would like to write a more complicated function for resolving file names, then a {transformer} would be a good fit.

caution

Specify as long fixed prefix ( with out /*/ ) as possible in path_specs.include. This will reduce the scanning time and cost, specifically on AWS S3

caution

Running profiling against many tables or over many rows can run up significant costs. While we've done our best to limit the expensiveness of the queries the profiler runs, you should be prudent about the set of tables profiling is enabled on or the frequency of the profiling runs.

caution

If you are ingesting datasets from AWS S3, we recommend running the ingestion on a server in the same region to avoid high egress costs.

Compatibility​

Profiles are computed with PyDeequ, which relies on PySpark. Therefore, for computing profiles, we currently require Spark 3.0.3 with Hadoop 3.2 to be installed and the SPARK_HOME and SPARK_VERSION environment variables to be set. The Spark+Hadoop binary can be downloaded here.

For an example guide on setting up PyDeequ on AWS, see this guide.

Code Coordinates​

  • Class Name: datahub.ingestion.source.s3.source.S3Source
  • Browse on GitHub

Questions

If you've got any questions on configuring ingestion for S3 Data Lake, feel free to ping us on our Slack.