from .common_bucket_s3_pickle_io_manager import common_bucket_s3_pickle_io_manager
from .parquet_io_manager import (
local_partitioned_parquet_io_manager,
s3_partitioned_parquet_io_manager,
)
from .snowflake_io_manager import snowflake_io_manager
configured_pyspark = pyspark_resource.configured({
"spark_conf": {
"spark.jars.packages":
",".join([
"net.snowflake:snowflake-jdbc:3.8.0",
"net.snowflake:spark-snowflake_2.12:2.8.2-spark_3.0",
"com.amazonaws:aws-java-sdk:1.7.4,org.apache.hadoop:hadoop-aws:2.7.7",
]),
"spark.hadoop.fs.s3.impl":
"org.apache.hadoop.fs.s3native.NativeS3FileSystem",
"spark.hadoop.fs.s3.awsAccessKeyId":
os.getenv("AWS_ACCESS_KEY_ID", ""),
"spark.hadoop.fs.s3.awsSecretAccessKey":
os.getenv("AWS_SECRET_ACCESS_KEY", ""),
"spark.hadoop.fs.s3.buffer.dir":
"/tmp",
}
})
snowflake_io_manager_prod = snowflake_io_manager.configured(
{"database": "DEMO_DB_ASSETS"})
RESOURCES_PROD = {
"s3_bucket":
ResourceDefinition.hardcoded_resource("hackernews-elementl-prod"),
def count_people(people: DataFrame) -> int:
return people.count()
emr_resource_defs = {
"pyspark_step_launcher": emr_pyspark_step_launcher.configured(
{
"cluster_id": {"env": "EMR_CLUSTER_ID"},
"local_pipeline_package_path": str(Path(__file__).parent),
"deploy_local_pipeline_package": True,
"region_name": "us-west-1",
"staging_bucket": "my_staging_bucket",
"wait_for_logs": True,
}
),
"pyspark": pyspark_resource.configured({"spark_conf": {"spark.executor.memory": "2g"}}),
"s3": s3_resource,
"io_manager": s3_pickle_io_manager.configured(
{"s3_bucket": "my_staging_bucket", "s3_prefix": "simple-pyspark"}
),
}
local_resource_defs = {
"pyspark_step_launcher": no_step_launcher,
"pyspark": pyspark_resource.configured({"spark_conf": {"spark.default.parallelism": 1}}),
}
@graph
def count_people_over_50():
count_people(filter_over_50(make_people()))
"warehouse_io_manager": fs_io_manager,
"pyspark": pyspark_resource,
"hn_client": hn_api_subsample_client.configured({"sample_rate": 10}),
}
PROD_RESOURCES = {
"io_manager": s3_pickle_io_manager.configured({"s3_bucket": "hackernews-elementl-prod"}),
"s3": s3_resource,
"partition_start": ResourceDefinition.string_resource(),
"partition_end": ResourceDefinition.string_resource(),
"parquet_io_manager": partitioned_parquet_io_manager.configured(
{"base_path": "s3://hackernews-elementl-prod"}
),
"warehouse_io_manager": time_partitioned_snowflake_io_manager_prod,
"pyspark": pyspark_resource.configured(S3_SPARK_CONF),
"hn_client": hn_api_subsample_client.configured({"sample_rate": 10}),
}
download_pipeline_properties = {
"description": "#### Owners:\n"
"[email protected], [email protected]\n "
"#### About\n"
"This pipeline downloads all items from the HN API for a given day, "
"splits the items into stories and comment types using Spark, and uploads filtered items to "
"the corresponding stories or comments Snowflake table",
"tags": {
"dagster-k8s/config": {
"container_config": {
"resources": {
"requests": {"cpu": "500m", "memory": "2Gi"},
