DynamicFrame kelas

Salah satu abstraksi utama dalam Apache Spark adalah Spark SQLDataFrame, yang mirip dengan konstruksi yang ditemukan di R dan DataFrame Pandas. A DataFrame mirip dengan tabel dan mendukung operasi dan operasi gaya fungsional (map/reduce/filter/dll.) (Pilih, proyek, agregat). SQL

DataFrameskuat dan banyak digunakan, tetapi mereka memiliki keterbatasan sehubungan dengan operasi ekstrak, transformasi, dan beban (ETL). Yang paling signifikan, mereka memerlukan skema yang akan ditentukan sebelum data dimuat. Spark SQL mengatasi ini dengan membuat dua lintasan data — yang pertama menyimpulkan skema, dan yang kedua memuat data. Namun demikian, kesimpulan ini terbatas dan tidak mengatasi realitas data yang berantakan. Misalnya, bidang yang sama mungkin mempunyai jenis yang berbeda dalam catatan yang berbeda. Apache Spark sering kali menyerah dan melaporkan jenis sebagai string dengan menggunakan teks bidang asli. Ini mungkin tidak benar, dan Anda mungkin ingin kontrol yang lebih baik atas bagaimana perbedaan skema diselesaikan. Dan untuk set data besar, pemberian tambahan atas sumber data mungkin mahal.

Untuk mengatasi keterbatasan ini, AWS Glue memperkenalkan. DynamicFrame Sebuah DynamicFrame mirip dengan DataFrame, kecuali bahwa setiap catatan bersifat self-describing, sehingga tidak ada skema yang diperlukan diawal. Sebagai gantinya, AWS Glue menghitung skema on-the-fly bila diperlukan, dan secara eksplisit mengkodekan inkonsistensi skema menggunakan jenis pilihan (atau gabungan). Anda dapat mengatasi inkonsistensi ini untuk membuat set data Anda kompatibel dengan penyimpanan data yang memerlukan sebuah skema tetap.

Demikian pula, DynamicRecord mewakili sebuah catatan logis dalam sebuah DynamicFrame. Hal ini seperti sebuah baris dalam DataFrame Spark, kecuali bahwa ia bersifat self-describing dan dapat digunakan untuk data yang tidak sesuai dengan sebuah skema tetap. Saat menggunakan AWS Glue with PySpark, Anda biasanya tidak memanipulasi secara independenDynamicRecords. Sebaliknya, Anda akan mengubah kumpulan data bersama-sama melaluinyaDynamicFrame.

Anda dapat mengonversi DynamicFrames ke dan dari DataFrames setelah Anda menyelesaikan inkonsistensi skema.

 — konstruksi —

__init__

fromDF

toDF

 — Informasi —

count

count( ) — Mengembalikan jumlah baris dalam DataFrame yang mendasari.

schema

schema( ) — Mengembalikan skema dari DynamicFrame ini, atau jika itu tidak tersedia, skema dari DataFrame yang mendasari.

Untuk informasi selengkapnya tentang DynamicFrame jenis yang membentuk skema ini, lihatPySpark jenis ekstensi.

printSchema

printSchema( ) — Mencetak skema dari DataFrame yang mendasari.

show

show(num_rows) — Mencetak sejumlah baris yang ditentukan dari DataFrame yang mendasari.

repartition

repartition(numPartitions) — Mengembalikan DynamicFrame yang baru dengan partisi numPartitions.

coalesce

coalesce(numPartitions) — Mengembalikan DynamicFrame yang baru dengan partisi numPartitions.

 — transformasi —

apply_mapping

Contoh: Gunakan apply_mapping untuk mengganti nama bidang dan mengubah jenis bidang

Contoh kode berikut menunjukkan cara menggunakan apply_mapping metode untuk mengganti nama bidang yang dipilih dan mengubah jenis bidang.

# Example: Use apply_mapping to reshape source data into
# the desired column names and types as a new DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Select and rename fields, change field type
print("Schema for the persons_mapped DynamicFrame, created with apply_mapping:")
persons_mapped = persons.apply_mapping(
    [
        ("family_name", "String", "last_name", "String"),
        ("name", "String", "first_name", "String"),
        ("birth_date", "String", "date_of_birth", "Date"),
    ]
)
persons_mapped.printSchema()

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the persons_mapped DynamicFrame, created with apply_mapping:
root
|-- last_name: string
|-- first_name: string
|-- date_of_birth: date
          

drop_fields

Contoh: Gunakan drop_fields untuk menghapus bidang dari DynamicFrame

Contoh kode ini menggunakan drop_fields metode untuk menghapus bidang tingkat atas dan bersarang yang dipilih dari a. DynamicFrame

Contoh dataset

Contoh menggunakan dataset berikut yang diwakili oleh EXAMPLE-FRIENDS-DATA tabel dalam kode:

{"name": "Sally", "age": 23, "location": {"state": "WY", "county": "Fremont"}, "friends": []}
{"name": "Varun", "age": 34, "location": {"state": "NE", "county": "Douglas"}, "friends": [{"name": "Arjun", "age": 3}]}
{"name": "George", "age": 52, "location": {"state": "NY"}, "friends": [{"name": "Fred"}, {"name": "Amy", "age": 15}]}
{"name": "Haruki", "age": 21, "location": {"state": "AK", "county": "Denali"}}
{"name": "Sheila", "age": 63, "friends": [{"name": "Nancy", "age": 22}]}

Contoh kode

# Example: Use drop_fields to remove top-level and nested fields from a DynamicFrame.
# Replace MY-EXAMPLE-DATABASE with your Glue Data Catalog database name.
# Replace EXAMPLE-FRIENDS-DATA with your table name.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame from Glue Data Catalog
glue_source_database = "MY-EXAMPLE-DATABASE"
glue_source_table = "EXAMPLE-FRIENDS-DATA"

friends = glueContext.create_dynamic_frame.from_catalog(
    database=glue_source_database, table_name=glue_source_table
)
print("Schema for friends DynamicFrame before calling drop_fields:")
friends.printSchema()

# Remove location.county, remove friends.age, remove age
friends = friends.drop_fields(paths=["age", "location.county", "friends.age"])
print("Schema for friends DynamicFrame after removing age, county, and friend age:")
friends.printSchema()

Schema for friends DynamicFrame before calling drop_fields:
root
|-- name: string
|-- age: int
|-- location: struct
|    |-- state: string
|    |-- county: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string
|    |    |-- age: int

Schema for friends DynamicFrame after removing age, county, and friend age:
root
|-- name: string
|-- location: struct
|    |-- state: string
|-- friends: array
|    |-- element: struct
|    |    |-- name: string
          

filter

Contoh: Gunakan filter untuk mendapatkan pilihan bidang yang difilter

Contoh ini menggunakan filter metode untuk membuat baru DynamicFrame yang mencakup pemilihan bidang lain DynamicFrame yang difilter.

Seperti map metode, filter mengambil fungsi sebagai argumen yang akan diterapkan untuk setiap record dalam aslinyaDynamicFrame. Fungsi ini mengambil catatan sebagai input dan mengembalikan nilai Boolean. Jika nilai pengembalian benar, catatan akan dimasukkan dalam hasilDynamicFrame. Jika salah, catatan ditinggalkan.

# Example: Use filter to create a new DynamicFrame
# with a filtered selection of records

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create DynamicFrame from Glue Data Catalog
medicare = glueContext.create_dynamic_frame.from_options(
    "s3",
    {
        "paths": [
            "s3://awsglue-datasets/examples/medicare/Medicare_Hospital_Provider.csv"
        ]
    },
    "csv",
    {"withHeader": True},
)

# Create filtered DynamicFrame with custom lambda
# to filter records by Provider State and Provider City
sac_or_mon = medicare.filter(
    f=lambda x: x["Provider State"] in ["CA", "AL"]
    and x["Provider City"] in ["SACRAMENTO", "MONTGOMERY"]
)

# Compare record counts
print("Unfiltered record count: ", medicare.count())
print("Filtered record count:  ", sac_or_mon.count())

Unfiltered record count:  163065
Filtered record count:   564         

join

Contoh: Gunakan bergabung untuk menggabungkan DynamicFrames

Contoh ini menggunakan join metode untuk melakukan join pada tigaDynamicFrames. AWS Glue melakukan gabungan berdasarkan tombol bidang yang Anda berikan. Hasilnya DynamicFrame berisi baris dari dua frame asli di mana kunci yang ditentukan cocok.

Perhatikan bahwa join transformasi membuat semua bidang tetap utuh. Ini berarti bahwa bidang yang Anda tentukan untuk dicocokkan muncul di hasil DynamicFrame, meskipun mereka berlebihan dan berisi kunci yang sama. Dalam contoh ini, kita gunakan drop_fields untuk menghapus kunci redundan ini setelah bergabung.

# Example: Use join to combine data from three DynamicFrames

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Load DynamicFrames from Glue Data Catalog
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
memberships = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="memberships_json"
)
orgs = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="organizations_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()
print("Schema for the memberships DynamicFrame:")
memberships.printSchema()
print("Schema for the orgs DynamicFrame:")
orgs.printSchema()

# Join persons and memberships by ID
persons_memberships = persons.join(
    paths1=["id"], paths2=["person_id"], frame2=memberships
)

# Rename and drop fields from orgs
# to prevent field name collisions with persons_memberships
orgs = (
    orgs.drop_fields(["other_names", "identifiers"])
    .rename_field("id", "org_id")
    .rename_field("name", "org_name")
)

# Create final join of all three DynamicFrames
legislators_combined = orgs.join(
    paths1=["org_id"], paths2=["organization_id"], frame2=persons_memberships
).drop_fields(["person_id", "org_id"])

# Inspect the schema for the joined data
print("Schema for the new legislators_combined DynamicFrame:")
legislators_combined.printSchema()

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the memberships DynamicFrame:
root
|-- area_id: string
|-- on_behalf_of_id: string
|-- organization_id: string
|-- role: string
|-- person_id: string
|-- legislative_period_id: string
|-- start_date: string
|-- end_date: string

Schema for the orgs DynamicFrame:
root
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- id: string
|-- classification: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- image: string
|-- seats: int
|-- type: string

Schema for the new legislators_combined DynamicFrame:
root
|-- role: string
|-- seats: int
|-- org_name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- type: string
|-- sort_name: string
|-- area_id: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- on_behalf_of_id: string
|-- other_names: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- name: string
|    |    |-- lang: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- name: string
|-- birth_date: string
|-- organization_id: string
|-- gender: string
|-- classification: string
|-- legislative_period_id: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- image: string
|-- given_name: string
|-- start_date: string
|-- family_name: string
|-- id: string
|-- death_date: string
|-- end_date: string

map

Contoh: Gunakan peta untuk menerapkan fungsi ke setiap catatan dalam DynamicFrame

Contoh ini menunjukkan bagaimana menggunakan map metode untuk menerapkan fungsi untuk setiap record dari sebuahDynamicFrame. Secara khusus, contoh ini menerapkan fungsi yang dipanggil MergeAddress ke setiap catatan untuk menggabungkan beberapa bidang alamat menjadi satu struct jenis.

# Example: Use map to combine fields in all records
# of a DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
medicare = glueContext.create_dynamic_frame.from_options(
        "s3",
        {"paths": ["s3://awsglue-datasets/examples/medicare/Medicare_Hospital_Provider.csv"]},
        "csv",
        {"withHeader": True})
print("Schema for medicare DynamicFrame:")
medicare.printSchema()

# Define a function to supply to the map transform
# that merges address fields into a single field
def MergeAddress(rec):
  rec["Address"] = {}
  rec["Address"]["Street"] = rec["Provider Street Address"]
  rec["Address"]["City"] = rec["Provider City"]
  rec["Address"]["State"] = rec["Provider State"]
  rec["Address"]["Zip.Code"] = rec["Provider Zip Code"]
  rec["Address"]["Array"] = [rec["Provider Street Address"], rec["Provider City"], rec["Provider State"], rec["Provider Zip Code"]]
  del rec["Provider Street Address"]
  del rec["Provider City"]
  del rec["Provider State"]
  del rec["Provider Zip Code"]
  return rec


# Use map to apply MergeAddress to every record
mapped_medicare = medicare.map(f = MergeAddress)
print("Schema for mapped_medicare DynamicFrame:")
mapped_medicare.printSchema()

Schema for medicare DynamicFrame:
root
|-- DRG Definition: string
|-- Provider Id: string
|-- Provider Name: string
|-- Provider Street Address: string
|-- Provider City: string
|-- Provider State: string
|-- Provider Zip Code: string
|-- Hospital Referral Region Description: string
|-- Total Discharges: string
|-- Average Covered Charges: string
|-- Average Total Payments: string
|-- Average Medicare Payments: string

Schema for mapped_medicare DynamicFrame:
root
|-- Average Total Payments: string
|-- Average Covered Charges: string
|-- DRG Definition: string
|-- Average Medicare Payments: string
|-- Hospital Referral Region Description: string
|-- Address: struct
|    |-- Zip.Code: string
|    |-- City: string
|    |-- Array: array
|    |    |-- element: string
|    |-- State: string
|    |-- Street: string
|-- Provider Id: string
|-- Total Discharges: string
|-- Provider Name: string

mergeDynamicFrame

Metode ini mengembalikan baru DynamicFrame yang diperoleh dengan menggabungkan ini DynamicFrame dengan DynamicFrame pementasan.

DynamicFrame yang dikembalikan berisi catatan A dalam kasus ini:

Bingkai sumber dan bingkai pementasan tidak perlu memiliki skema yang sama.

Contoh: Gunakan mergeDynamicFrame untuk menggabungkan dua DynamicFrames berdasarkan kunci utama

Contoh kode berikut menunjukkan bagaimana menggunakan mergeDynamicFrame metode untuk menggabungkan DynamicFrame dengan “pementasan”DynamicFrame, berdasarkan kunci utama. id

Contoh dataset

Contoh menggunakan dua DynamicFrames dari yang DynamicFrameCollection dipanggilsplit_rows_collection. Berikut ini adalah daftar kunci displit_rows_collection.

dict_keys(['high', 'low'])

Contoh kode

# Example: Use mergeDynamicFrame to merge DynamicFrames
# based on a set of specified primary keys

from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.transforms import SelectFromCollection

# Inspect the original DynamicFrames
frame_low = SelectFromCollection.apply(dfc=split_rows_collection, key="low")
print("Inspect the DynamicFrame that contains rows where ID < 10")
frame_low.toDF().show()

frame_high = SelectFromCollection.apply(dfc=split_rows_collection, key="high")
print("Inspect the DynamicFrame that contains rows where ID > 10")
frame_high.toDF().show()

# Merge the DynamicFrames based on the "id" primary key
merged_high_low = frame_high.mergeDynamicFrame(
    stage_dynamic_frame=frame_low, primary_keys=["id"]
)

# View the results where the ID is 1 or 20
print("Inspect the merged DynamicFrame that contains the combined rows")
merged_high_low.toDF().where("id = 1 or id= 20").orderBy("id").show()

Inspect the DynamicFrame that contains rows where ID < 10
+---+-----+------------------------+-------------------------+
| id|index|contact_details.val.type|contact_details.val.value|
+---+-----+------------------------+-------------------------+
|  1|    0|                     fax|             202-225-3307|
|  1|    1|                   phone|             202-225-5731|
|  2|    0|                     fax|             202-225-3307|
|  2|    1|                   phone|             202-225-5731|
|  3|    0|                     fax|             202-225-3307|
|  3|    1|                   phone|             202-225-5731|
|  4|    0|                     fax|             202-225-3307|
|  4|    1|                   phone|             202-225-5731|
|  5|    0|                     fax|             202-225-3307|
|  5|    1|                   phone|             202-225-5731|
|  6|    0|                     fax|             202-225-3307|
|  6|    1|                   phone|             202-225-5731|
|  7|    0|                     fax|             202-225-3307|
|  7|    1|                   phone|             202-225-5731|
|  8|    0|                     fax|             202-225-3307|
|  8|    1|                   phone|             202-225-5731|
|  9|    0|                     fax|             202-225-3307|
|  9|    1|                   phone|             202-225-5731|
| 10|    0|                     fax|             202-225-6328|
| 10|    1|                   phone|             202-225-4576|
+---+-----+------------------------+-------------------------+
only showing top 20 rows

Inspect the DynamicFrame that contains rows where ID > 10
+---+-----+------------------------+-------------------------+
| id|index|contact_details.val.type|contact_details.val.value|
+---+-----+------------------------+-------------------------+
| 11|    0|                     fax|             202-225-6328|
| 11|    1|                   phone|             202-225-4576|
| 11|    2|                 twitter|           RepTrentFranks|
| 12|    0|                     fax|             202-225-6328|
| 12|    1|                   phone|             202-225-4576|
| 12|    2|                 twitter|           RepTrentFranks|
| 13|    0|                     fax|             202-225-6328|
| 13|    1|                   phone|             202-225-4576|
| 13|    2|                 twitter|           RepTrentFranks|
| 14|    0|                     fax|             202-225-6328|
| 14|    1|                   phone|             202-225-4576|
| 14|    2|                 twitter|           RepTrentFranks|
| 15|    0|                     fax|             202-225-6328|
| 15|    1|                   phone|             202-225-4576|
| 15|    2|                 twitter|           RepTrentFranks|
| 16|    0|                     fax|             202-225-6328|
| 16|    1|                   phone|             202-225-4576|
| 16|    2|                 twitter|           RepTrentFranks|
| 17|    0|                     fax|             202-225-6328|
| 17|    1|                   phone|             202-225-4576|
+---+-----+------------------------+-------------------------+
only showing top 20 rows

Inspect the merged DynamicFrame that contains the combined rows
+---+-----+------------------------+-------------------------+
| id|index|contact_details.val.type|contact_details.val.value|
+---+-----+------------------------+-------------------------+
|  1|    0|                     fax|             202-225-3307|
|  1|    1|                   phone|             202-225-5731|
| 20|    0|                     fax|             202-225-5604|
| 20|    1|                   phone|             202-225-6536|
| 20|    2|                 twitter|                USRepLong|
+---+-----+------------------------+-------------------------+
          

relationalize

Contoh: Gunakan relationalize untuk meratakan skema bersarang di DynamicFrame

Contoh kode ini menggunakan relationalize metode untuk meratakan skema bersarang menjadi bentuk yang cocok dengan database relasional.

Contoh dataset

Contoh menggunakan DynamicFrame dipanggil legislators_combined dengan skema berikut. legislators_combinedmemiliki beberapa bidang bersarang sepertilinks,images, dancontact_details, yang akan diratakan oleh transformasi. relationalize

root
|-- role: string
|-- seats: int
|-- org_name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- type: string
|-- sort_name: string
|-- area_id: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- on_behalf_of_id: string
|-- other_names: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- name: string
|    |    |-- lang: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- name: string
|-- birth_date: string
|-- organization_id: string
|-- gender: string
|-- classification: string
|-- legislative_period_id: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- image: string
|-- given_name: string
|-- start_date: string
|-- family_name: string
|-- id: string
|-- death_date: string
|-- end_date: string

Contoh kode

# Example: Use relationalize to flatten
# a nested schema into a format that fits
# into a relational database.
# Replace DOC-EXAMPLE-S3-BUCKET/tmpDir with your own location.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Apply relationalize and inspect new tables
legislators_relationalized = legislators_combined.relationalize(
    "l_root", "s3://DOC-EXAMPLE-BUCKET/tmpDir"
)
legislators_relationalized.keys()

# Compare the schema of the contact_details
# nested field to the new relationalized table that
# represents it
legislators_combined.select_fields("contact_details").printSchema()
legislators_relationalized.select("l_root_contact_details").toDF().where(
    "id = 10 or id = 75"
).orderBy(["id", "index"]).show()

dict_keys(['l_root', 'l_root_images', 'l_root_links', 'l_root_other_names', 'l_root_contact_details', 'l_root_identifiers'])

root
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string

+---+-----+------------------------+-------------------------+
| id|index|contact_details.val.type|contact_details.val.value|
+---+-----+------------------------+-------------------------+
| 10|    0|                     fax|             202-225-4160|
| 10|    1|                   phone|             202-225-3436|
| 75|    0|                     fax|             202-225-6791|
| 75|    1|                   phone|             202-225-2861|
| 75|    2|                 twitter|               RepSamFarr|
+---+-----+------------------------+-------------------------+

rename_field

Contoh: Gunakan rename_field untuk mengganti nama bidang dalam DynamicFrame

Contoh kode ini menggunakan rename_field metode untuk mengganti nama bidang dalamDynamicFrame. Perhatikan bahwa contoh menggunakan metode chaining untuk mengganti nama beberapa bidang secara bersamaan.

Contoh kode

# Example: Use rename_field to rename fields
# in a DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Inspect the original orgs schema
orgs = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="organizations_json"
)
print("Original orgs schema: ")
orgs.printSchema()

# Rename fields and view the new schema
orgs = orgs.rename_field("id", "org_id").rename_field("name", "org_name")
print("New orgs schema with renamed fields: ")
orgs.printSchema()

Original orgs schema: 
root
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- id: string
|-- classification: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- image: string
|-- seats: int
|-- type: string

New orgs schema with renamed fields: 
root
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- classification: string
|-- org_id: string
|-- org_name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- image: string
|-- seats: int
|-- type: string
          

resolveChoice

Contoh: Gunakan resolveChoice untuk menangani kolom yang berisi beberapa jenis

Contoh kode ini menggunakan resolveChoice metode untuk menentukan cara menangani DynamicFrame kolom yang berisi nilai-nilai dari beberapa jenis. Contoh menunjukkan dua cara umum untuk menangani kolom dengan jenis yang berbeda:

Contoh dataset

Contoh menggunakan DynamicFrame dipanggil medicare dengan skema berikut:

root
|-- drg definition: string
|-- provider id: choice
|    |-- long
|    |-- string
|-- provider name: string
|-- provider street address: string
|-- provider city: string
|-- provider state: string
|-- provider zip code: long
|-- hospital referral region description: string
|-- total discharges: long
|-- average covered charges: string
|-- average total payments: string
|-- average medicare payments: string

Contoh kode

# Example: Use resolveChoice to handle
# a column that contains multiple types

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Load the input data and inspect the "provider id" column
medicare = glueContext.create_dynamic_frame.from_catalog(
    database="payments", table_name="medicare_hospital_provider_csv"
)
print("Inspect the provider id column:")
medicare.toDF().select("provider id").show()

# Cast provider id to type long
medicare_resolved_long = medicare.resolveChoice(specs=[("provider id", "cast:long")])
print("Schema after casting provider id to type long:")
medicare_resolved_long.printSchema()
medicare_resolved_long.toDF().select("provider id").show()

# Create separate columns
# for each provider id type
medicare_resolved_cols = medicare.resolveChoice(choice="make_cols")
print("Schema after creating separate columns for each type:")
medicare_resolved_cols.printSchema()
medicare_resolved_cols.toDF().select("provider id_long", "provider id_string").show()

Inspect the 'provider id' column:
+-----------+
|provider id|
+-----------+
|   [10001,]|
|   [10005,]|
|   [10006,]|
|   [10011,]|
|   [10016,]|
|   [10023,]|
|   [10029,]|
|   [10033,]|
|   [10039,]|
|   [10040,]|
|   [10046,]|
|   [10055,]|
|   [10056,]|
|   [10078,]|
|   [10083,]|
|   [10085,]|
|   [10090,]|
|   [10092,]|
|   [10100,]|
|   [10103,]|
+-----------+
only showing top 20 rows

Schema after casting 'provider id' to type long:
root
|-- drg definition: string
|-- provider id: long
|-- provider name: string
|-- provider street address: string
|-- provider city: string
|-- provider state: string
|-- provider zip code: long
|-- hospital referral region description: string
|-- total discharges: long
|-- average covered charges: string
|-- average total payments: string
|-- average medicare payments: string

+-----------+
|provider id|
+-----------+
|      10001|
|      10005|
|      10006|
|      10011|
|      10016|
|      10023|
|      10029|
|      10033|
|      10039|
|      10040|
|      10046|
|      10055|
|      10056|
|      10078|
|      10083|
|      10085|
|      10090|
|      10092|
|      10100|
|      10103|
+-----------+
only showing top 20 rows

Schema after creating separate columns for each type:
root
|-- drg definition: string
|-- provider id_string: string
|-- provider id_long: long
|-- provider name: string
|-- provider street address: string
|-- provider city: string
|-- provider state: string
|-- provider zip code: long
|-- hospital referral region description: string
|-- total discharges: long
|-- average covered charges: string
|-- average total payments: string
|-- average medicare payments: string

+----------------+------------------+
|provider id_long|provider id_string|
+----------------+------------------+
|           10001|              null|
|           10005|              null|
|           10006|              null|
|           10011|              null|
|           10016|              null|
|           10023|              null|
|           10029|              null|
|           10033|              null|
|           10039|              null|
|           10040|              null|
|           10046|              null|
|           10055|              null|
|           10056|              null|
|           10078|              null|
|           10083|              null|
|           10085|              null|
|           10090|              null|
|           10092|              null|
|           10100|              null|
|           10103|              null|
+----------------+------------------+
only showing top 20 rows
          

select_fields

Contoh: Gunakan select_fields untuk membuat baru DynamicFrame dengan bidang yang dipilih

Contoh kode berikut menunjukkan cara menggunakan select_fields metode untuk membuat yang baru DynamicFrame dengan daftar bidang yang dipilih dari yang sudah adaDynamicFrame.

# Example: Use select_fields to select specific fields from a DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create a DynamicFrame and view its schema
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)
print("Schema for the persons DynamicFrame:")
persons.printSchema()

# Create a new DynamicFrame with chosen fields
names = persons.select_fields(paths=["family_name", "given_name"])
print("Schema for the names DynamicFrame, created with select_fields:")
names.printSchema()
names.toDF().show()

Schema for the persons DynamicFrame:
root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Schema for the names DynamicFrame:
root
|-- family_name: string
|-- given_name: string

+-----------+----------+
|family_name|given_name|
+-----------+----------+
|    Collins|   Michael|
|   Huizenga|      Bill|
|    Clawson|    Curtis|
|    Solomon|    Gerald|
|     Rigell|    Edward|
|      Crapo|   Michael|
|      Hutto|      Earl|
|      Ertel|     Allen|
|     Minish|    Joseph|
|    Andrews|    Robert|
|     Walden|      Greg|
|      Kazen|   Abraham|
|     Turner|   Michael|
|      Kolbe|     James|
|  Lowenthal|      Alan|
|    Capuano|   Michael|
|   Schrader|      Kurt|
|     Nadler|   Jerrold|
|     Graves|       Tom|
|   McMillan|      John|
+-----------+----------+
only showing top 20 rows
          

sederhana_ddb_json

simplify_ddb_json(): DynamicFrame

Menyederhanakan kolom bersarang dalam DynamicFrame yang secara khusus dalam JSON struktur DynamoDB, dan mengembalikan disederhanakan baru. DynamicFrame Jika ada beberapa jenis atau tipe Peta dalam tipe Daftar, elemen dalam Daftar tidak akan disederhanakan. Perhatikan bahwa ini adalah jenis transformasi tertentu yang berperilaku berbeda dari unnest transformasi biasa dan mengharuskan data sudah berada dalam struktur DynamoDBJSON. Untuk informasi selengkapnya, lihat DynamoDB JSON.

Misalnya, skema pembacaan ekspor dengan struktur JSON DynamoDB mungkin terlihat seperti berikut:

root
|-- Item: struct
|    |-- parentMap: struct
|    |    |-- M: struct
|    |    |    |-- childMap: struct
|    |    |    |    |-- M: struct
|    |    |    |    |    |-- appName: struct
|    |    |    |    |    |    |-- S: string
|    |    |    |    |    |-- packageName: struct
|    |    |    |    |    |    |-- S: string
|    |    |    |    |    |-- updatedAt: struct
|    |    |    |    |    |    |-- N: string
|    |-- strings: struct
|    |    |-- SS: array
|    |    |    |-- element: string
|    |-- numbers: struct
|    |    |-- NS: array
|    |    |    |-- element: string
|    |-- binaries: struct
|    |    |-- BS: array
|    |    |    |-- element: string
|    |-- isDDBJson: struct
|    |    |-- BOOL: boolean
|    |-- nullValue: struct
|    |    |-- NULL: boolean

simplify_ddb_json()Transformasi akan mengubah ini menjadi:

root
|-- parentMap: struct
|    |-- childMap: struct
|    |    |-- appName: string
|    |    |-- packageName: string
|    |    |-- updatedAt: string
|-- strings: array
|    |-- element: string
|-- numbers: array
|    |-- element: string
|-- binaries: array
|    |-- element: string
|-- isDDBJson: boolean
|-- nullValue: null

Contoh: Gunakan simplify_ddb_json untuk memanggil DynamoDB menyederhanakan JSON

Contoh kode ini menggunakan simplify_ddb_json metode untuk menggunakan konektor ekspor AWS Glue DynamoDB, memanggil DynamoDB menyederhanakan, dan mencetak JSON jumlah partisi.

Contoh kode

from pyspark.context import SparkContext
from awsglue.context import GlueContext

sc = SparkContext()
glueContext = GlueContext(sc)

dynamicFrame = glueContext.create_dynamic_frame.from_options(
    connection_type = "dynamodb",
    connection_options = {
        'dynamodb.export': 'ddb',
        'dynamodb.tableArn': '<table arn>',
        'dynamodb.s3.bucket': '<bucket name>',
        'dynamodb.s3.prefix': '<bucket prefix>',
        'dynamodb.s3.bucketOwner': '<account_id of bucket>'
    }
)
simplified = dynamicFrame.simplify_ddb_json()
print(simplified.getNumPartitions())

spigot

Contoh: Gunakan keran untuk menulis bidang contoh dari a DynamicFrame ke Amazon S3

Contoh kode ini menggunakan spigot metode untuk menulis catatan sampel ke bucket Amazon S3 setelah menerapkan transformasi. select_fields

Contoh dataset

Contoh menggunakan DynamicFrame dipanggil persons dengan skema berikut:

root
|-- family_name: string
|-- name: string
|-- links: array
|    |-- element: struct
|    |    |-- note: string
|    |    |-- url: string
|-- gender: string
|-- image: string
|-- identifiers: array
|    |-- element: struct
|    |    |-- scheme: string
|    |    |-- identifier: string
|-- other_names: array
|    |-- element: struct
|    |    |-- lang: string
|    |    |-- note: string
|    |    |-- name: string
|-- sort_name: string
|-- images: array
|    |-- element: struct
|    |    |-- url: string
|-- given_name: string
|-- birth_date: string
|-- id: string
|-- contact_details: array
|    |-- element: struct
|    |    |-- type: string
|    |    |-- value: string
|-- death_date: string

Contoh kode

# Example: Use spigot to write sample records
# to a destination during a transformation
# from pyspark.context import SparkContext.
# Replace DOC-EXAMPLE-BUCKET with your own location.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Load table data into a DynamicFrame
persons = glueContext.create_dynamic_frame.from_catalog(
    database="legislators", table_name="persons_json"
)

# Perform the select_fields on the DynamicFrame
persons = persons.select_fields(paths=["family_name", "given_name", "birth_date"])

# Use spigot to write a sample of the transformed data
# (the first 10 records)
spigot_output = persons.spigot(
    path="s3://DOC-EXAMPLE-BUCKET", options={"topk": 10}
)

{"family_name":"Collins","given_name":"Michael","birth_date":"1944-10-15"}
{"family_name":"Huizenga","given_name":"Bill","birth_date":"1969-01-31"}
{"family_name":"Clawson","given_name":"Curtis","birth_date":"1959-09-28"}
{"family_name":"Solomon","given_name":"Gerald","birth_date":"1930-08-14"}
{"family_name":"Rigell","given_name":"Edward","birth_date":"1960-05-28"}
{"family_name":"Crapo","given_name":"Michael","birth_date":"1951-05-20"}
{"family_name":"Hutto","given_name":"Earl","birth_date":"1926-05-12"}
{"family_name":"Ertel","given_name":"Allen","birth_date":"1937-11-07"}
{"family_name":"Minish","given_name":"Joseph","birth_date":"1916-09-01"}
{"family_name":"Andrews","given_name":"Robert","birth_date":"1957-08-04"}
          

split_fields

Contoh: Gunakan split_fields untuk membagi bidang yang dipilih menjadi terpisah DynamicFrame

Contoh kode ini menggunakan split_fields metode untuk membagi daftar bidang tertentu menjadi terpisahDynamicFrame.

Contoh dataset

Contoh menggunakan panggilan DynamicFrame l_root_contact_details yang berasal dari koleksi bernamalegislators_relationalized.

l_root_contact_detailsmemiliki skema dan entri berikut.

root
|-- id: long
|-- index: int
|-- contact_details.val.type: string
|-- contact_details.val.value: string

+---+-----+------------------------+-------------------------+
| id|index|contact_details.val.type|contact_details.val.value|
+---+-----+------------------------+-------------------------+
|  1|    0|                   phone|             202-225-5265|
|  1|    1|                 twitter|              kathyhochul|
|  2|    0|                   phone|             202-225-3252|
|  2|    1|                 twitter|            repjackyrosen|
|  3|    0|                     fax|             202-225-1314|
|  3|    1|                   phone|             202-225-3772|
...

Contoh kode

# Example: Use split_fields to split selected
# fields into a separate DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Load the input DynamicFrame and inspect its schema
frame_to_split = legislators_relationalized.select("l_root_contact_details")
print("Inspect the input DynamicFrame schema:")
frame_to_split.printSchema()

# Split id and index fields into a separate DynamicFrame
split_fields_collection = frame_to_split.split_fields(["id", "index"], "left", "right")

# Inspect the resulting DynamicFrames
print("Inspect the schemas of the DynamicFrames created with split_fields:")
split_fields_collection.select("left").printSchema()
split_fields_collection.select("right").printSchema()

Inspect the input DynamicFrame's schema:
root
|-- id: long
|-- index: int
|-- contact_details.val.type: string
|-- contact_details.val.value: string

Inspect the schemas of the DynamicFrames created with split_fields:
root
|-- id: long
|-- index: int

root
|-- contact_details.val.type: string
|-- contact_details.val.value: string
          

split_rows

Contoh: Gunakan split_rows untuk membagi baris dalam DynamicFrame

Contoh kode ini menggunakan split_rows metode untuk membagi baris DynamicFrame berdasarkan nilai id bidang.

Contoh dataset

Contoh menggunakan panggilan DynamicFrame l_root_contact_details yang dipilih dari koleksi bernamalegislators_relationalized.

l_root_contact_detailsmemiliki skema dan entri berikut.

root
|-- id: long
|-- index: int
|-- contact_details.val.type: string
|-- contact_details.val.value: string

+---+-----+------------------------+-------------------------+
| id|index|contact_details.val.type|contact_details.val.value|
+---+-----+------------------------+-------------------------+
|  1|    0|                   phone|             202-225-5265|
|  1|    1|                 twitter|              kathyhochul|
|  2|    0|                   phone|             202-225-3252|
|  2|    1|                 twitter|            repjackyrosen|
|  3|    0|                     fax|             202-225-1314|
|  3|    1|                   phone|             202-225-3772|
|  3|    2|                 twitter|          MikeRossUpdates|
|  4|    0|                     fax|             202-225-1314|
|  4|    1|                   phone|             202-225-3772|
|  4|    2|                 twitter|          MikeRossUpdates|
|  5|    0|                     fax|             202-225-1314|
|  5|    1|                   phone|             202-225-3772|
|  5|    2|                 twitter|          MikeRossUpdates|
|  6|    0|                     fax|             202-225-1314|
|  6|    1|                   phone|             202-225-3772|
|  6|    2|                 twitter|          MikeRossUpdates|
|  7|    0|                     fax|             202-225-1314|
|  7|    1|                   phone|             202-225-3772|
|  7|    2|                 twitter|          MikeRossUpdates|
|  8|    0|                     fax|             202-225-1314|
+---+-----+------------------------+-------------------------+

Contoh kode

# Example: Use split_rows to split up 
# rows in a DynamicFrame based on value

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Retrieve the DynamicFrame to split
frame_to_split = legislators_relationalized.select("l_root_contact_details")

# Split up rows by ID
split_rows_collection = frame_to_split.split_rows({"id": {">": 10}}, "high", "low")

# Inspect the resulting DynamicFrames
print("Inspect the DynamicFrame that contains IDs < 10")
split_rows_collection.select("low").toDF().show()
print("Inspect the DynamicFrame that contains IDs > 10")
split_rows_collection.select("high").toDF().show()

Inspect the DynamicFrame that contains IDs < 10
+---+-----+------------------------+-------------------------+
| id|index|contact_details.val.type|contact_details.val.value|
+---+-----+------------------------+-------------------------+
|  1|    0|                   phone|             202-225-5265|
|  1|    1|                 twitter|              kathyhochul|
|  2|    0|                   phone|             202-225-3252|
|  2|    1|                 twitter|            repjackyrosen|
|  3|    0|                     fax|             202-225-1314|
|  3|    1|                   phone|             202-225-3772|
|  3|    2|                 twitter|          MikeRossUpdates|
|  4|    0|                     fax|             202-225-1314|
|  4|    1|                   phone|             202-225-3772|
|  4|    2|                 twitter|          MikeRossUpdates|
|  5|    0|                     fax|             202-225-1314|
|  5|    1|                   phone|             202-225-3772|
|  5|    2|                 twitter|          MikeRossUpdates|
|  6|    0|                     fax|             202-225-1314|
|  6|    1|                   phone|             202-225-3772|
|  6|    2|                 twitter|          MikeRossUpdates|
|  7|    0|                     fax|             202-225-1314|
|  7|    1|                   phone|             202-225-3772|
|  7|    2|                 twitter|          MikeRossUpdates|
|  8|    0|                     fax|             202-225-1314|
+---+-----+------------------------+-------------------------+
only showing top 20 rows

Inspect the DynamicFrame that contains IDs > 10
+---+-----+------------------------+-------------------------+
| id|index|contact_details.val.type|contact_details.val.value|
+---+-----+------------------------+-------------------------+
| 11|    0|                   phone|             202-225-5476|
| 11|    1|                 twitter|            RepDavidYoung|
| 12|    0|                   phone|             202-225-4035|
| 12|    1|                 twitter|           RepStephMurphy|
| 13|    0|                     fax|             202-226-0774|
| 13|    1|                   phone|             202-225-6335|
| 14|    0|                     fax|             202-226-0774|
| 14|    1|                   phone|             202-225-6335|
| 15|    0|                     fax|             202-226-0774|
| 15|    1|                   phone|             202-225-6335|
| 16|    0|                     fax|             202-226-0774|
| 16|    1|                   phone|             202-225-6335|
| 17|    0|                     fax|             202-226-0774|
| 17|    1|                   phone|             202-225-6335|
| 18|    0|                     fax|             202-226-0774|
| 18|    1|                   phone|             202-225-6335|
| 19|    0|                     fax|             202-226-0774|
| 19|    1|                   phone|             202-225-6335|
| 20|    0|                     fax|             202-226-0774|
| 20|    1|                   phone|             202-225-6335|
+---+-----+------------------------+-------------------------+
only showing top 20 rows
          

unbox

Contoh: Gunakan unbox untuk membuka kotak bidang string ke dalam struct

Contoh kode ini menggunakan unbox metode untuk membuka kotak, atau memformat ulang, bidang string dalam bidang DynamicFrame ke dalam bidang tipe struct.

Contoh dataset

Contoh menggunakan DynamicFrame dipanggil mapped_with_string dengan skema dan entri berikut.

Perhatikan bidang bernamaAddressString. Ini adalah bidang yang membuka kotak contoh menjadi struct.

root
|-- Average Total Payments: string
|-- AddressString: string
|-- Average Covered Charges: string
|-- DRG Definition: string
|-- Average Medicare Payments: string
|-- Hospital Referral Region Description: string
|-- Address: struct
|    |-- Zip.Code: string
|    |-- City: string
|    |-- Array: array
|    |    |-- element: string
|    |-- State: string
|    |-- Street: string
|-- Provider Id: string
|-- Total Discharges: string
|-- Provider Name: string

+----------------------+--------------------+-----------------------+--------------------+-------------------------+------------------------------------+--------------------+-----------+----------------+--------------------+
|Average Total Payments|       AddressString|Average Covered Charges|      DRG Definition|Average Medicare Payments|Hospital Referral Region Description|             Address|Provider Id|Total Discharges|       Provider Name|
+----------------------+--------------------+-----------------------+--------------------+-------------------------+------------------------------------+--------------------+-----------+----------------+--------------------+
|              $5777.24|{"Street": "1108 ...|              $32963.07|039 - EXTRACRANIA...|                 $4763.73|                         AL - Dothan|[36301, DOTHAN, [...|      10001|              91|SOUTHEAST ALABAMA...|
|              $5787.57|{"Street": "2505 ...|              $15131.85|039 - EXTRACRANIA...|                 $4976.71|                     AL - Birmingham|[35957, BOAZ, [25...|      10005|              14|MARSHALL MEDICAL ...|
|              $5434.95|{"Street": "205 M...|              $37560.37|039 - EXTRACRANIA...|                 $4453.79|                     AL - Birmingham|[35631, FLORENCE,...|      10006|              24|ELIZA COFFEE MEMO...|
|              $5417.56|{"Street": "50 ME...|              $13998.28|039 - EXTRACRANIA...|                 $4129.16|                     AL - Birmingham|[35235, BIRMINGHA...|      10011|              25|   ST VINCENT'S EAST|
...

Contoh kode

# Example: Use unbox to unbox a string field
# into a struct in a DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

unboxed = mapped_with_string.unbox("AddressString", "json")
unboxed.printSchema()
unboxed.toDF().show()

root
|-- Average Total Payments: string
|-- AddressString: struct
|    |-- Street: string
|    |-- City: string
|    |-- State: string
|    |-- Zip.Code: string
|    |-- Array: array
|    |    |-- element: string
|-- Average Covered Charges: string
|-- DRG Definition: string
|-- Average Medicare Payments: string
|-- Hospital Referral Region Description: string
|-- Address: struct
|    |-- Zip.Code: string
|    |-- City: string
|    |-- Array: array
|    |    |-- element: string
|    |-- State: string
|    |-- Street: string
|-- Provider Id: string
|-- Total Discharges: string
|-- Provider Name: string

+----------------------+--------------------+-----------------------+--------------------+-------------------------+------------------------------------+--------------------+-----------+----------------+--------------------+
|Average Total Payments|       AddressString|Average Covered Charges|      DRG Definition|Average Medicare Payments|Hospital Referral Region Description|             Address|Provider Id|Total Discharges|       Provider Name|
+----------------------+--------------------+-----------------------+--------------------+-------------------------+------------------------------------+--------------------+-----------+----------------+--------------------+
|              $5777.24|[1108 ROSS CLARK ...|              $32963.07|039 - EXTRACRANIA...|                 $4763.73|                         AL - Dothan|[36301, DOTHAN, [...|      10001|              91|SOUTHEAST ALABAMA...|
|              $5787.57|[2505 U S HIGHWAY...|              $15131.85|039 - EXTRACRANIA...|                 $4976.71|                     AL - Birmingham|[35957, BOAZ, [25...|      10005|              14|MARSHALL MEDICAL ...|
|              $5434.95|[205 MARENGO STRE...|              $37560.37|039 - EXTRACRANIA...|                 $4453.79|                     AL - Birmingham|[35631, FLORENCE,...|      10006|              24|ELIZA COFFEE MEMO...|
|              $5417.56|[50 MEDICAL PARK ...|              $13998.28|039 - EXTRACRANIA...|                 $4129.16|                     AL - Birmingham|[35235, BIRMINGHA...|      10011|              25|   ST VINCENT'S EAST|
|              $5658.33|[1000 FIRST STREE...|              $31633.27|039 - EXTRACRANIA...|                 $4851.44|                     AL - Birmingham|[35007, ALABASTER...|      10016|              18|SHELBY BAPTIST ME...|
|              $6653.80|[2105 EAST SOUTH ...|              $16920.79|039 - EXTRACRANIA...|                 $5374.14|                     AL - Montgomery|[36116, MONTGOMER...|      10023|              67|BAPTIST MEDICAL C...|
|              $5834.74|[2000 PEPPERELL P...|              $11977.13|039 - EXTRACRANIA...|                 $4761.41|                     AL - Birmingham|[36801, OPELIKA, ...|      10029|              51|EAST ALABAMA MEDI...|
|              $8031.12|[619 SOUTH 19TH S...|              $35841.09|039 - EXTRACRANIA...|                 $5858.50|                     AL - Birmingham|[35233, BIRMINGHA...|      10033|              32|UNIVERSITY OF ALA...|
|              $6113.38|[101 SIVLEY RD, H...|              $28523.39|039 - EXTRACRANIA...|                 $5228.40|                     AL - Huntsville|[35801, HUNTSVILL...|      10039|             135| HUNTSVILLE HOSPITAL|
|              $5541.05|[1007 GOODYEAR AV...|              $75233.38|039 - EXTRACRANIA...|                 $4386.94|                     AL - Birmingham|[35903, GADSDEN, ...|      10040|              34|GADSDEN REGIONAL ...|
|              $5461.57|[600 SOUTH THIRD ...|              $67327.92|039 - EXTRACRANIA...|                 $4493.57|                     AL - Birmingham|[35901, GADSDEN, ...|      10046|              14|RIVERVIEW REGIONA...|
|              $5356.28|[4370 WEST MAIN S...|              $39607.28|039 - EXTRACRANIA...|                 $4408.20|                         AL - Dothan|[36305, DOTHAN, [...|      10055|              45|    FLOWERS HOSPITAL|
|              $5374.65|[810 ST VINCENT'S...|              $22862.23|039 - EXTRACRANIA...|                 $4186.02|                     AL - Birmingham|[35205, BIRMINGHA...|      10056|              43|ST VINCENT'S BIRM...|
|              $5366.23|[400 EAST 10TH ST...|              $31110.85|039 - EXTRACRANIA...|                 $4376.23|                     AL - Birmingham|[36207, ANNISTON,...|      10078|              21|NORTHEAST ALABAMA...|
|              $5282.93|[1613 NORTH MCKEN...|              $25411.33|039 - EXTRACRANIA...|                 $4383.73|                         AL - Mobile|[36535, FOLEY, [1...|      10083|              15|SOUTH BALDWIN REG...|
|              $5676.55|[1201 7TH STREET ...|               $9234.51|039 - EXTRACRANIA...|                 $4509.11|                     AL - Huntsville|[35609, DECATUR, ...|      10085|              27|DECATUR GENERAL H...|
|              $5930.11|[6801 AIRPORT BOU...|              $15895.85|039 - EXTRACRANIA...|                 $3972.85|                         AL - Mobile|[36608, MOBILE, [...|      10090|              27| PROVIDENCE HOSPITAL|
|              $6192.54|[809 UNIVERSITY B...|              $19721.16|039 - EXTRACRANIA...|                 $5179.38|                     AL - Tuscaloosa|[35401, TUSCALOOS...|      10092|              31|D C H REGIONAL ME...|
|              $4968.00|[750 MORPHY AVENU...|              $10710.88|039 - EXTRACRANIA...|                 $3898.88|                         AL - Mobile|[36532, FAIRHOPE,...|      10100|              18|     THOMAS HOSPITAL|
|              $5996.00|[701 PRINCETON AV...|              $51343.75|039 - EXTRACRANIA...|                 $4962.45|                     AL - Birmingham|[35211, BIRMINGHA...|      10103|              33|BAPTIST MEDICAL C...|
+----------------------+--------------------+-----------------------+--------------------+-------------------------+------------------------------------+--------------------+-----------+----------------+--------------------+
only showing top 20 rows
          

serikat

unnest

Contoh: Gunakan unnest untuk mengubah bidang bersarang menjadi bidang tingkat atas

Contoh kode ini menggunakan unnest metode untuk meratakan semua bidang bersarang di dalam bidang tingkat atas. DynamicFrame

Contoh dataset

Contoh menggunakan DynamicFrame dipanggil mapped_medicare dengan skema berikut. Perhatikan bahwa Address bidang adalah satu-satunya bidang yang berisi data bersarang.

root
|-- Average Total Payments: string
|-- Average Covered Charges: string
|-- DRG Definition: string
|-- Average Medicare Payments: string
|-- Hospital Referral Region Description: string
|-- Address: struct
|    |-- Zip.Code: string
|    |-- City: string
|    |-- Array: array
|    |    |-- element: string
|    |-- State: string
|    |-- Street: string
|-- Provider Id: string
|-- Total Discharges: string
|-- Provider Name: string

Contoh kode

# Example: Use unnest to unnest nested
# objects in a DynamicFrame

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Unnest all nested fields
unnested = mapped_medicare.unnest()
unnested.printSchema()

root
|-- Average Total Payments: string
|-- Average Covered Charges: string
|-- DRG Definition: string
|-- Average Medicare Payments: string
|-- Hospital Referral Region Description: string
|-- Address.Zip.Code: string
|-- Address.City: string
|-- Address.Array: array
|    |-- element: string
|-- Address.State: string
|-- Address.Street: string
|-- Provider Id: string
|-- Total Discharges: string
|-- Provider Name: string
          

unnest_ddb_json

Unnests kolom bersarang di a DynamicFrame yang secara khusus dalam JSON struktur DynamoDB, dan mengembalikan unnested baru. DynamicFrame Kolom yang terdiri dari array tipe struct tidak akan di-unnested. Perhatikan bahwa ini adalah jenis transformasi unnesting tertentu yang berperilaku berbeda dari unnest transformasi biasa dan mengharuskan data sudah berada dalam struktur DynamoDB. JSON Untuk informasi selengkapnya, lihat DynamoDB JSON.

Misalnya, skema pembacaan ekspor dengan struktur JSON DynamoDB mungkin terlihat seperti berikut:

root
|-- Item: struct
|    |-- ColA: struct
|    |    |-- S: string
|    |-- ColB: struct
|    |    |-- S: string
|    |-- ColC: struct
|    |    |-- N: string
|    |-- ColD: struct
|    |    |-- L: array
|    |    |    |-- element: null

unnest_ddb_json()Transformasi akan mengubah ini menjadi:

root
|-- ColA: string
|-- ColB: string
|-- ColC: string
|-- ColD: array    
|    |-- element: null

Contoh kode berikut menunjukkan cara menggunakan konektor ekspor AWS Glue DynamoDB, memanggil DynamoDB unnest, dan mencetak JSON jumlah partisi:

import sys
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
from awsglue.utils import getResolvedOptions

args = getResolvedOptions(sys.argv, ["JOB_NAME"])
glue_context= GlueContext(SparkContext.getOrCreate())
job = Job(glue_context)
job.init(args["JOB_NAME"], args)

dynamicFrame = glue_context.create_dynamic_frame.from_options(
    connection_type="dynamodb",
    connection_options={
        "dynamodb.export": "ddb",
        "dynamodb.tableArn": "<test_source>",
        "dynamodb.s3.bucket": "<bucket name>",
        "dynamodb.s3.prefix": "<bucket prefix>",
        "dynamodb.s3.bucketOwner": "<account_id>",
    }
)
unnested = dynamicFrame.unnest_ddb_json()
print(unnested.getNumPartitions())

job.commit()

tulis

 — kesalahan —

assertErrorThreshold

assertErrorThreshold( ) — Sebuah pernyataan untuk kesalahan dalam transformasi yang menciptakan DynamicFrame ini. Mengembalikan sebuah Exception dari DataFrame yang mendasari.

errorsAsDynamicBingkai

errorsAsDynamicFrame( ) — Mengembalikan DynamicFrame yang memiliki catatan kesalahan yang di-nest di dalamnya.

Contoh: Gunakan errorsAsDynamic Frame untuk melihat catatan kesalahan

Contoh kode berikut menunjukkan cara menggunakan errorsAsDynamicFrame metode untuk melihat catatan kesalahan untuk fileDynamicFrame.

Contoh dataset

Contoh menggunakan kumpulan data berikut yang dapat Anda unggah ke Amazon JSON S3 sebagai. Perhatikan bahwa catatan kedua cacat. Data yang salah bentuk biasanya merusak penguraian file saat Anda menggunakan Spark. SQL Namun, DynamicFrame mengenali masalah malformasi dan mengubah garis cacat menjadi catatan kesalahan yang dapat Anda tangani satu per satu.

{"id": 1, "name": "george", "surname": "washington", "height": 178}
{"id": 2, "name": "benjamin", "surname": "franklin", 
{"id": 3, "name": "alexander", "surname": "hamilton", "height": 171}
{"id": 4, "name": "john", "surname": "jay", "height": 190}

Contoh kode

# Example: Use errorsAsDynamicFrame to view error records.
# Replace s3://DOC-EXAMPLE-S3-BUCKET/error_data.json with your location.

from pyspark.context import SparkContext
from awsglue.context import GlueContext

# Create GlueContext
sc = SparkContext.getOrCreate()
glueContext = GlueContext(sc)

# Create errors DynamicFrame, view schema
errors = glueContext.create_dynamic_frame.from_options(
    "s3", {"paths": ["s3://DOC-EXAMPLE-S3-BUCKET/error_data.json"]}, "json"
)
print("Schema of errors DynamicFrame:")
errors.printSchema()

# Show that errors only contains valid entries from the dataset
print("errors contains only valid records from the input dataset (2 of 4 records)")
errors.toDF().show()

# View errors
print("Errors count:", str(errors.errorsCount()))
print("Errors:")
errors.errorsAsDynamicFrame().toDF().show()

# View error fields and error data
error_record = errors.errorsAsDynamicFrame().toDF().head()

error_fields = error_record["error"]
print("Error fields: ")
print(error_fields.asDict().keys())

print("\nError record data:")
for key in error_fields.asDict().keys():
    print("\n", key, ": ", str(error_fields[key]))

Schema of errors DynamicFrame:
root
|-- id: int
|-- name: string
|-- surname: string
|-- height: int

errors contains only valid records from the input dataset (2 of 4 records)
+---+------+----------+------+
| id|  name|   surname|height|
+---+------+----------+------+
|  1|george|washington|   178|
|  4|  john|       jay|   190|
+---+------+----------+------+

Errors count: 1
Errors:
+--------------------+
|               error|
+--------------------+
|[[  File "/tmp/20...|
+--------------------+

Error fields: 
dict_keys(['callsite', 'msg', 'stackTrace', 'input', 'bytesread', 'source', 'dynamicRecord'])

Error record data:

 callsite :  Row(site='  File "/tmp/2060612586885849088", line 549, in <module>\n    sys.exit(main())\n  File "/tmp/2060612586885849088", line 523, in main\n    response = handler(content)\n  File "/tmp/2060612586885849088", line 197, in execute_request\n    result = node.execute()\n  File "/tmp/2060612586885849088", line 103, in execute\n    exec(code, global_dict)\n  File "<stdin>", line 10, in <module>\n  File "/opt/amazon/lib/python3.6/site-packages/awsglue/dynamicframe.py", line 625, in from_options\n    format_options, transformation_ctx, push_down_predicate, **kwargs)\n  File "/opt/amazon/lib/python3.6/site-packages/awsglue/context.py", line 233, in create_dynamic_frame_from_options\n    source.setFormat(format, **format_options)\n', info='')

 msg :  error in jackson reader

 stackTrace :  com.fasterxml.jackson.core.JsonParseException: Unexpected character ('{' (code 123)): was expecting either valid name character (for unquoted name) or double-quote (for quoted) to start field name
 at [Source: com.amazonaws.services.glue.readers.BufferedStream@73492578; line: 3, column: 2]
	at com.fasterxml.jackson.core.JsonParser._constructError(JsonParser.java:1581)
	at com.fasterxml.jackson.core.base.ParserMinimalBase._reportError(ParserMinimalBase.java:533)
	at com.fasterxml.jackson.core.base.ParserMinimalBase._reportUnexpectedChar(ParserMinimalBase.java:462)
	at com.fasterxml.jackson.core.json.UTF8StreamJsonParser._handleOddName(UTF8StreamJsonParser.java:2012)
	at com.fasterxml.jackson.core.json.UTF8StreamJsonParser._parseName(UTF8StreamJsonParser.java:1650)
	at com.fasterxml.jackson.core.json.UTF8StreamJsonParser.nextToken(UTF8StreamJsonParser.java:740)
	at com.amazonaws.services.glue.readers.JacksonReader$$anonfun$hasNextGoodToken$1.apply(JacksonReader.scala:57)
	at com.amazonaws.services.glue.readers.JacksonReader$$anonfun$hasNextGoodToken$1.apply(JacksonReader.scala:57)
	at scala.collection.Iterator$$anon$9.next(Iterator.scala:162)
	at scala.collection.Iterator$$anon$16.hasNext(Iterator.scala:599)
	at scala.collection.Iterator$$anon$16.hasNext(Iterator.scala:598)
	at scala.collection.Iterator$class.foreach(Iterator.scala:891)
	at scala.collection.AbstractIterator.foreach(Iterator.scala:1334)
	at com.amazonaws.services.glue.readers.JacksonReader$$anonfun$1.apply(JacksonReader.scala:120)
	at com.amazonaws.services.glue.readers.JacksonReader$$anonfun$1.apply(JacksonReader.scala:116)
	at com.amazonaws.services.glue.DynamicRecordBuilder.handleErr(DynamicRecordBuilder.scala:209)
	at com.amazonaws.services.glue.DynamicRecordBuilder.handleErrorWithException(DynamicRecordBuilder.scala:202)
	at com.amazonaws.services.glue.readers.JacksonReader.nextFailSafe(JacksonReader.scala:116)
	at com.amazonaws.services.glue.readers.JacksonReader.next(JacksonReader.scala:109)
	at com.amazonaws.services.glue.readers.JSONReader.next(JSONReader.scala:247)
	at com.amazonaws.services.glue.hadoop.TapeHadoopRecordReaderSplittable.nextKeyValue(TapeHadoopRecordReaderSplittable.scala:103)
	at org.apache.spark.rdd.NewHadoopRDD$$anon$1.hasNext(NewHadoopRDD.scala:230)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
	at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:462)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
	at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:462)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
	at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
	at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:121)
	at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:750)


 input :  

 bytesread :  252

 source :  

 dynamicRecord :  Row(id=2, name='benjamin', surname='franklin')
          

errorsCount

errorsCount( ) — Mengembalikan jumlah kesalahan dalam sebuah DynamicFrame.

stageErrorsCount

stageErrorsCount — Mengembalikan jumlah kesalahan yang terjadi dalam proses membuat DynamicFrame ini.