Kinesis Firehose continuously stream json files to S3 bucket. Using pyspark I need to convert into parquet and write to another bucket. Plannig to run this EMR job once in every 30 mins. I used the following code, but EMR job is running long(more than 4 hours and then i killed the job) to convert small files(Around 400K small files and total size is around 30GB).
Can someone give your input to increase the performance of the job?
Code:
# Loop through all folders
for k in sourceBucket.list():
source = k.bucket.name + '/' + k.key
srcsubfolder = source.split('/')
if srcsubfolder[2] != '':
sourcePath = 's3://' + k.bucket.name + '/' + k.key
# derive target path
removefilenamefromkey = k.key.split('/')[:-1]
targetPath = 's3://' + targetBucket.name +'/' + '/'.join(removefilenamefromkey)
#Covert Json into Parquet files
dfjson = spark.read.json(sourcePath)
dfjsoncollower = dfjson.toDF(*[x.lower() for x in dfjson.columns])
dfjsoncollower.write.mode("append").parquet(targetPath)
Related
I created a streaming pipeline which does the following actions:
get pubsub messages containing addresses of csv files
read the corresponding csv files
write the content of the csv files to a BigQuery table
My pipeline code looks like this:
def run():
options = PipelineOptions( save_main_session=True, streaming=False, autoscaling_algorithm='THROUGHPUT_BASED',max_num_workers=500)
options.view_as(GoogleCloudOptions).project = 'my_project'
options.view_as(GoogleCloudOptions).region = 'europe-west1'
options.view_as(GoogleCloudOptions).staging_location = 'staging_address'
options.view_as(GoogleCloudOptions).temp_location = 'temp_address'
options.view_as(StandardOptions).runner = 'DataflowRunner'
p = beam.Pipeline(options=options)
road = (p | 'ReadFromPubSub' >> beam.io.ReadFromPubSub('projects/my_project/topics/my_topic')
| 'ParseJson' >> beam.Map(parse_json)
| 'GetFileAddress' >> beam.Map(lambda element: element["fileAddress"])
| 'ReadCSVFiles' >> beam.io.ReadAllFromText(skip_header_lines=1)
| 'FormatLines' >> beam.Map(read_line)
| 'WriteAggToBQ1' >> beam.io.WriteToBigQuery(
destination_table,
schema=schema,
create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED,
write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND
)
)
p.run()
if __name__ == '__main__':
run()
When running the pipeline on Dataflow and trying to read big csv files (50 GB), the pipeline never scales and keeps being stuck at one worker.
Why does Dataflow refuse to scale in this situation?
I am working with Azure Data Lake Gen2, .NET client (Azure.Storage.Files.DataLake 12.8.0)
Given the following F# code, running on premises.
open Azure.Storage.Files.DataLake
.
.
.
let azdlc = DataLakeServiceClient(System.Uri(dfsUri), sharedKeyCredential)
for i = 1 to 2 do
let fs = azdlc.GetFileSystemClient(repo)
fs.CreateIfNotExists() |> ignore // > 20s when i = 1
let dir = (fsCli repo).GetDirectoryClient(directory)
dir.CreateIfNotExists() |> ignore // > 20s when i = 1
let file = (dirCli repo directory).GetFileClient(fileName)
file.CreateIfNotExists() |> ignore
the first time (i = 1) CrateIfNotExists() runs for FileSystem and Directory (not for File), the call lasts more than 20s. After the first call (i > 1) they last much less (less than 1s).
This regardless of the resource exist already or not.
It seems too long to me. Is it normal?
I have an ETL job where I load some data from S3 into a dynamic frame, relationalize it, and iterate through the dynamic frames returned. I want to query the result of this in Athena later so I want to change the names of the columns from having '.' to '_' and lower case them. When I do this transformation, I change the DynamicFrame into a spark dataframe and have been doing it this way. I've also seen a problem in another SO question where it turned out there is a reported problem with AWS Glue rename field transform so I've stayed away from that.
I've tried a couple things, including adding a load limit size to 50MB, repartitioning the dataframe, using both dataframe.schema.names and dataframe.columns, using reduce instead of loops, using sparksql to change it and nothing has worked. I'm fairly certain that its this transformation that failing because I've put some print statements in and the print that I have right after the completion of this transformation never shows up. I used a UDF at one point but that also failed. I've tried the actual transformation using df.toDF(new_column_names) and df.withColumnRenamed() but it never gets this far because I've not seen it get past retrieving the column names. Here's the code I've been using. I've been changing the actual name transformation as I said above, but the rest of it has stayed pretty much the same.
I've seen some people try and use the spark.executor.memory, spark.driver.memory, spark.executor.memoryOverhead and spark.driver.memoryOverhead. I've used those and set them to the most AWS Glue will let you but to no avail.
import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
from awsglue.dynamicframe import DynamicFrame
from pyspark.sql.functions import explode, col, lower, trim, regexp_replace
import copy
import json
import boto3
import botocore
import time
# ========================================================
# UTILITY FUNCTIONS
# ========================================================
def lower_and_pythonize(s=None):
if s is not None:
return s.replace('.', '_').lower()
else:
return None
# pyspark implementation of renaming
# exprs = [
# regexp_replace(lower(trim(col(c))),'\.' , '_').alias(c) if t == "string" else col(c)
# for (c, t) in data_frame.dtypes
# ]
# ========================================================
# END UTILITY FUNCTIONS
# ========================================================
## #params: [JOB_NAME]
args = getResolvedOptions(sys.argv, ['JOB_NAME'])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)
#my params
bucket_name = '<my-s3-bucket>' # name of the bucket. do not include 's3://' thats added later
output_key = '<my-output-path>' # key where all of the output is saved
input_keys = ['<root-directory-i'm using'] # highest level key that holds all of the desired data
s3_exclusions = "[\"*.orc\"]" # list of strings to exclude. Documentation: https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-connect.html#aws-glue-programming-etl-connect-s3
s3_exclusions = s3_exclusions.replace('\n', '')
dfc_root_table_name = 'root' # name of the root table generated in the relationalize process
input_paths = ['s3://' + bucket_name + '/' + x for x in input_keys] # turn input keys into s3 paths
output_connection_opts = {"path": "s3://" + bucket_name + "/" + output_key} # dict of options. Documentation link found above the write_dynamic_frame.from_options line
s3_client = boto3.client('s3', 'us-east-1') # s3 client used for writing to s3
s3_resource = boto3.resource('s3', 'us-east-1') # s3 resource used for checking if key exists
group_mb = 50 # NOTE: 75 has proven to be too much when running on all of the april data
group_size = str(group_mb * 1024 * 1024)
input_connection_opts = {'paths': input_paths,
'groupFiles': 'inPartition',
'groupSize': group_size,
'recurse': True,
'exclusions': s3_exclusions} # dict of options. Documentation link found above the create_dynamic_frame_from_options line
print(sc._conf.get('spark.executor.cores'))
num_paritions = int(sc._conf.get('spark.executor.cores')) * 4
print('Loading all json files into DynamicFrame...')
loading_time = time.time()
df = glueContext.create_dynamic_frame_from_options(connection_type='s3', connection_options=input_connection_opts, format='json')
print('Done. Time to complete: {}s'.format(time.time() - loading_time))
# using the list of known null fields (at least on small sample size) remove them
#df = df.drop_fields(drop_paths)
# drop any remaining null fields. The above covers known problems that this step doesn't fix
print('Dropping null fields...')
dropping_time = time.time()
df_without_null = DropNullFields.apply(frame=df, transformation_ctx='df_without_null')
print('Done. Time to complete: {}s'.format(time.time() - dropping_time))
df = None
print('Relationalizing dynamic frame...')
relationalizing_time = time.time()
dfc = Relationalize.apply(frame=df_without_null, name=dfc_root_table_name, info="RELATIONALIZE", transformation_ctx='dfc', stageThreshold=3)
print('Done. Time to complete: {}s'.format(time.time() - relationalizing_time))
keys = dfc.keys()
keys.sort(key=lambda s: len(s))
print('Writting all dynamic frames to s3...')
writting_time = time.time()
for key in keys:
good_key = lower_and_pythonize(s=key)
data_frame = dfc.select(key).toDF()
# lowercase all the names and remove '.'
print('Removing . and _ from names for {} frame...'.format(key))
df_fix_names_time = time.time()
print('Repartitioning data frame...')
data_frame.repartition(num_paritions)
print('Done.')
#
print('Changing names...')
for old_name in data_frame.schema.names:
data_frame = data_frame.withColumnRenamed(old_name, old_name.replace('.','_').lower())
print('Done.')
#
df_now = DynamicFrame.fromDF(dataframe=data_frame, glue_ctx=glueContext, name='df_now')
print('Done. Time to complete: {}'.format(time.time() - df_fix_names_time))
# if a conflict of types appears, make it 2 columns
# https://docs.aws.amazon.com/glue/latest/dg/built-in-transforms.html
print('Fixing any type conficts for {} frame...'.format(key))
df_resolve_time = time.time()
resolved = ResolveChoice.apply(frame = df_now, choice = 'make_cols', transformation_ctx = 'resolved')
print('Done. Time to complete: {}'.format(time.time() - df_resolve_time))
# check if key exists in s3. if not make one
out_connect = copy.deepcopy(output_connection_opts)
out_connect['path'] = out_connect['path'] + '/' + str(good_key)
try:
s3_resource.Object(bucket_name, output_key + '/' + good_key + '/').load()
except botocore.exceptions.ClientError as e:
if e.response['Error']['Code'] == '404' or 'NoSuchKey' in e.response['Error']['Code']:
# object doesn't exist
s3_client.put_object(Bucket=bucket_name, Key=output_key+'/'+good_key + '/')
else:
print(e)
## https://docs.aws.amazon.com/glue/latest/dg/aws-glue-api-crawler-pyspark-extensions-glue-context.html
print('Writing {} frame to S3...'.format(key))
df_writing_time = time.time()
datasink4 = glueContext.write_dynamic_frame.from_options(frame = df_now, connection_type = "s3", connection_options = out_connect, format = "orc", transformation_ctx = "datasink4")
out_connect = None
datasink4 = None
print('Done. Time to complete: {}'.format(time.time() - df_writing_time))
print('Done. Time to complete: {}s'.format(time.time() - writting_time))
job.commit()
Here is the error I'm getting
19/06/07 16:33:36 DEBUG Client:
client token: N/A
diagnostics: Application application_1559921043869_0001 failed 1 times due to AM Container for appattempt_1559921043869_0001_000001 exited with exitCode: -104
For more detailed output, check application tracking page:http://ip-172-32-9-38.ec2.internal:8088/cluster/app/application_1559921043869_0001Then, click on links to logs of each attempt.
Diagnostics: Container [pid=9630,containerID=container_1559921043869_0001_01_000001] is running beyond physical memory limits. Current usage: 5.6 GB of 5.5 GB physical memory used; 8.8 GB of 27.5 GB virtual memory used. Killing container.
Dump of the process-tree for container_1559921043869_0001_01_000001 :
|- PID PPID PGRPID SESSID CMD_NAME USER_MODE_TIME(MILLIS) SYSTEM_TIME(MILLIS) VMEM_USAGE(BYTES) RSSMEM_USAGE(PAGES) FULL_CMD_LINE
|- 9630 9628 9630 9630 (bash) 0 0 115822592 675 /bin/bash -c LD_LIBRARY_PATH=/usr/lib/hadoop/lib/native:/usr/lib/hadoop-lzo/lib/native:::/usr/lib/hadoop-lzo/lib/native:/usr/lib/hadoop/lib/native::/usr/lib/hadoop-lzo/lib/native:/usr/lib/hadoop/lib/native:/usr/lib/hadoop-lzo/lib/native:/usr/lib/hadoop/lib/native /usr/lib/jvm/java-openjdk/bin/java -server -Xmx5120m -Djava.io.tmpdir=/mnt/yarn/usercache/root/appcache/application_1559921043869_0001/container_1559921043869_0001_01_000001/tmp '-XX:+UseConcMarkSweepGC' '-XX:CMSInitiatingOccupancyFraction=70' '-XX:MaxHeapFreeRatio=70' '-XX:+CMSClassUnloadingEnabled' '-XX:OnOutOfMemoryError=kill -9 %p' '-Djavax.net.ssl.trustStore=ExternalAndAWSTrustStore.jks' '-Djavax.net.ssl.trustStoreType=JKS' '-Djavax.net.ssl.trustStorePassword=amazon' '-DRDS_ROOT_CERT_PATH=rds-combined-ca-bundle.pem' '-DREDSHIFT_ROOT_CERT_PATH=redshift-ssl-ca-cert.pem' '-DRDS_TRUSTSTORE_URL=file:RDSTrustStore.jks' -Dspark.yarn.app.container.log.dir=/var/log/hadoop-yarn/containers/application_1559921043869_0001/container_1559921043869_0001_01_000001 org.apache.spark.deploy.yarn.ApplicationMaster --class 'org.apache.spark.deploy.PythonRunner' --primary-py-file runscript.py --arg 'script_2019-06-07-15-29-50.py' --arg '--JOB_NAME' --arg 'tss-json-to-orc' --arg '--JOB_ID' --arg 'j_f9f7363e5d8afa20784bc83d7821493f481a78352641ad2165f8f68b88c8e5fe' --arg '--JOB_RUN_ID' --arg 'jr_a77087792dd74231be1f68c1eda2ed33200126b8952c5b1420cb6684759cf233' --arg '--job-bookmark-option' --arg 'job-bookmark-disable' --arg '--TempDir' --arg 's3://aws-glue-temporary-059866946490-us-east-1/zmcgrath' --properties-file /mnt/yarn/usercache/root/appcache/application_1559921043869_0001/container_1559921043869_0001_01_000001/__spark_conf__/__spark_conf__.properties 1> /var/log/hadoop-yarn/containers/application_1559921043869_0001/container_1559921043869_0001_01_000001/stdout 2> /var/log/hadoop-yarn/containers/application_1559921043869_0001/container_1559921043869_0001_01_000001/stderr
|- 9677 9648 9630 9630 (python) 12352 2628 1418354688 261364 python runscript.py script_2019-06-07-15-29-50.py --JOB_NAME tss-json-to-orc --JOB_ID j_f9f7363e5d8afa20784bc83d7821493f481a78352641ad2165f8f68b88c8e5fe --JOB_RUN_ID jr_a77087792dd74231be1f68c1eda2ed33200126b8952c5b1420cb6684759cf233 --job-bookmark-option job-bookmark-disable --TempDir s3://aws-glue-temporary-059866946490-us-east-1/zmcgrath
|- 9648 9630 9630 9630 (java) 265906 3083 7916974080 1207439 /usr/lib/jvm/java-openjdk/bin/java -server -Xmx5120m -Djava.io.tmpdir=/mnt/yarn/usercache/root/appcache/application_1559921043869_0001/container_1559921043869_0001_01_000001/tmp -XX:+UseConcMarkSweepGC -XX:CMSInitiatingOccupancyFraction=70 -XX:MaxHeapFreeRatio=70 -XX:+CMSClassUnloadingEnabled -XX:OnOutOfMemoryError=kill -9 %p -Djavax.net.ssl.trustStore=ExternalAndAWSTrustStore.jks -Djavax.net.ssl.trustStoreType=JKS -Djavax.net.ssl.trustStorePassword=amazon -DRDS_ROOT_CERT_PATH=rds-combined-ca-bundle.pem -DREDSHIFT_ROOT_CERT_PATH=redshift-ssl-ca-cert.pem -DRDS_TRUSTSTORE_URL=file:RDSTrustStore.jks -Dspark.yarn.app.container.log.dir=/var/log/hadoop-yarn/containers/application_1559921043869_0001/container_1559921043869_0001_01_000001 org.apache.spark.deploy.yarn.ApplicationMaster --class org.apache.spark.deploy.PythonRunner --primary-py-file runscript.py --arg script_2019-06-07-15-29-50.py --arg --JOB_NAME --arg tss-json-to-orc --arg --JOB_ID --arg j_f9f7363e5d8afa20784bc83d7821493f481a78352641ad2165f8f68b88c8e5fe --arg --JOB_RUN_ID --arg jr_a77087792dd74231be1f68c1eda2ed33200126b8952c5b1420cb6684759cf233 --arg --job-bookmark-option --arg job-bookmark-disable --arg --TempDir --arg s3://aws-glue-temporary-059866946490-us-east-1/zmcgrath --properties-file /mnt/yarn/usercache/root/appcache/application_1559921043869_0001/container_1559921043869_0001_01_000001/__spark_conf__/__spark_conf__.properties
Container killed on request. Exit code is 143
Container exited with a non-zero exit code 143
Failing this attempt. Failing the application.
ApplicationMaster host: N/A
ApplicationMaster RPC port: -1
queue: default
start time: 1559921462650
final status: FAILED
tracking URL: http://ip-172-32-9-38.ec2.internal:8088/cluster/app/application_1559921043869_0001
user: root
Here are the log contents from the job
LogType:stdout
Log Upload Time:Fri Jun 07 16:33:36 +0000 2019
LogLength:487
Log Contents:
4
Loading all json files into DynamicFrame...
Done. Time to complete: 59.5056920052s
Dropping null fields...
null_fields [<some fields that were dropped>]
Done. Time to complete: 529.95293808s
Relationalizing dynamic frame...
Done. Time to complete: 2773.11689401s
Writting all dynamic frames to s3...
Removing . and _ from names for root frame...
Repartitioning data frame...
Done.
Changing names...
End of LogType:stdout
As I said earlier, the Done. print after changing the names never appears in the logs. I've seen plenty of people getting the same error I'm seeing and I've tried a fair bit of them with no success. Any help you can provide would b e much appreciated. Let me know if you need any more information. Thanks
Edit
Prabhakar's comment reminded me that I have tried the memory worker type in AWS Glue and it still failed. As stated above, I have tried raising the amount of memory in the memoryOverhead from 5 to 12, but to avail. Neither of these made the job complete successfully
Update
I put in the following code for column name change instead of the above code for easier debugging
print('Changing names...')
name_counter = 0
for old_name in data_frame.schema.names:
print('Name number {}. name being changed: {}'.format(name_counter, old_name))
data_frame = data_frame.withColumnRenamed(old_name, old_name.replace('.','_').lower())
name_counter += 1
print('Done.')
And I got the following output
Removing . and _ from names for root frame...
Repartitioning data frame...
Done.
Changing names...
End of LogType:stdout
So it must be a problem with the data_frame.schema.names part. Could it be this line with my loop through all of the DynamicFrames? Am I looping through the DynamicFrames from the relationalize transformation correctly?
Update 2
Glue recently added more verbose logs and I found this
ERROR YarnClusterScheduler: Lost executor 396 on ip-172-32-78-221.ec2.internal: Container killed by YARN for exceeding memory limits. 5.5 GB of 5.5 GB physical memory used. Consider boosting spark.yarn.executor.memoryOverhead.
This happens for more than just this executor too; it looks like almost all of them.
I can try to increase the executor memory overhead, but I would like to know why getting the column names results in an OOM error. I wouldn't think that something that trivial would take up that much memory?
Update
I attempted to run the job with both spark.driver.memoryOverhead=7g and spark.yarn.executor.memoryOverhead=7g and I again got an OOM error
I want to read the zip file dataset from the kaggle but I am unable to read that dataset:
import urllib
urllib.request.urlretrieve("https://www.kaggle.com/himanshupoddar/zomato-bangalore-restaurants/downloads/zomato-bangalore-restaurants.zip", "/tmp/zomato-bangalore-restaurants.zip")
then I run shell scripting to extracting the file:
%sh
unzip /tmp/zomato-bangalore-restaurants.zip
tail -n +2 zomato-bangalore-restaurants.csv > temp.csv
rm zomato-bangalore-restaurants.csv
Then I got an error:
Archive: /tmp/zomato-bangalore-restaurants.zip
End-of-central-directory signature not found. Either this file is not
a zipfile, or it constitutes one disk of a multi-part archive. In the
latter case the central directory and zipfile comment will be found on
the last disk(s) of this archive.
unzip: cannot find zipfile directory in one of /tmp/zomato-bangalore-restaurants.zip or
/tmp/zomato-bangalore-restaurants.zip.zip, and cannot find /tmp/zomato-bangalore-restaurants.zip.ZIP, period.
tail: cannot open 'zomato-bangalore-restaurants.csv' for reading: No such file or directory
rm: cannot remove 'zomato-bangalore-restaurants.csv': No such file or directory
Note: Attempt to download a file from Kaggle is blocked because you are not logged in yet.
Here is the script to download all the competition data sets.
from requests import get, post
from os import mkdir, remove
from os.path import exists
from shutil import rmtree
import zipfile
def purge_all_downloads(db_full_path):
# Removes all the downloaded datasets
if exists(db_full_path): rmtree(db_full_path)
def datasets_are_available_locally(db_full_path, datasets):
# Returns True only if all the competition datasets are available locally in Databricks CE
if not exists(db_full_path): return False
for df in datasets:
# Assumes all the datasets end with '.csv' extention
if not exists(db_full_path + df + '.csv'): return False
return True
def remove_zip_files(db_full_path, datasets):
for df in datasets:
remove(db_full_path + df + '.csv.zip')
def unzip(db_full_path, datasets):
for df in datasets:
with zipfile.ZipFile(db_full_path + df + '.csv.zip', 'r') as zf:
zf.extractall(db_full_path)
remove_zip_files(db_full_path, datasets)
def download_datasets(competition, db_full_path, datasets, username, password):
# Downloads the competition datasets if not availible locally
if datasets_are_available_locally(db_full_path, datasets):
print 'All the competition datasets have been downloaded, extraced and are ready for you !'
return
purge_all_downloads(db_full_path)
mkdir(db_full_path)
kaggle_info = {'UserName': username, 'Password': password}
for df in datasets:
url = (
'https://www.kaggle.com/account/login?ReturnUrl=' +
'/c/' + competition + '/download/'+ df + '.csv.zip'
)
request = post(url, data=kaggle_info, stream=True)
# write data to local file
with open(db_full_path + df + '.csv.zip', "w") as f:
for chunk in request.iter_content(chunk_size = 512 * 1024):
if chunk: f.write(chunk)
# extract competition data
unzip(db_full_path, datasets)
print('done !')
For more details, refer "Download the competition data sets directly".
Hope this helps.
I'm pretty happy with s3cmd, but there is one issue: How to copy all files from one S3 bucket to another? Is it even possible?
EDIT: I've found a way to copy files between buckets using Python with boto:
from boto.s3.connection import S3Connection
def copyBucket(srcBucketName, dstBucketName, maxKeys = 100):
conn = S3Connection(awsAccessKey, awsSecretKey)
srcBucket = conn.get_bucket(srcBucketName);
dstBucket = conn.get_bucket(dstBucketName);
resultMarker = ''
while True:
keys = srcBucket.get_all_keys(max_keys = maxKeys, marker = resultMarker)
for k in keys:
print 'Copying ' + k.key + ' from ' + srcBucketName + ' to ' + dstBucketName
t0 = time.clock()
dstBucket.copy_key(k.key, srcBucketName, k.key)
print time.clock() - t0, ' seconds'
if len(keys) < maxKeys:
print 'Done'
break
resultMarker = keys[maxKeys - 1].key
Syncing is almost as straight forward as copying. There are fields for ETag, size, and last-modified available for keys.
Maybe this helps others as well.
s3cmd sync s3://from/this/bucket/ s3://to/this/bucket/
For available options, please use:
$s3cmd --help
AWS CLI seems to do the job perfectly, and has the bonus of being an officially supported tool.
aws s3 sync s3://mybucket s3://backup-mybucket
http://docs.aws.amazon.com/cli/latest/reference/s3/sync.html
The answer with the most upvotes as I write this is this one:
s3cmd sync s3://from/this/bucket s3://to/this/bucket
It's a useful answer. But sometimes sync is not what you need (it deletes files, etc.). It took me a long time to figure out this non-scripting alternative to simply copy multiple files between buckets. (OK, in the case shown below it's not between buckets. It's between not-really-folders, but it works between buckets equally well.)
# Slightly verbose, slightly unintuitive, very useful:
s3cmd cp --recursive --exclude=* --include=file_prefix* s3://semarchy-inc/source1/ s3://semarchy-inc/target/
Explanation of the above command:
–recursiveIn my mind, my requirement is not recursive. I simply want multiple files. But recursive in this context just tells s3cmd cp to handle multiple files. Great.
–excludeIt’s an odd way to think of the problem. Begin by recursively selecting all files. Next, exclude all files. Wait, what?
–includeNow we’re talking. Indicate the file prefix (or suffix or whatever pattern) that you want to include.s3://sourceBucket/ s3://targetBucket/This part is intuitive enough. Though technically it seems to violate the documented example from s3cmd help which indicates that a source object must be specified:s3cmd cp s3://BUCKET1/OBJECT1 s3://BUCKET2[/OBJECT2]
You can also use the web interface to do so:
Go to the source bucket in the web interface.
Mark the files you want to copy (use shift and mouse clicks to mark several).
Press Actions->Copy.
Go to the destination bucket.
Press Actions->Paste.
That's it.
I needed to copy a very large bucket so I adapted the code in the question into a multi threaded version and put it up on GitHub.
https://github.com/paultuckey/s3-bucket-to-bucket-copy-py
It's actually possible. This worked for me:
import boto
AWS_ACCESS_KEY = 'Your access key'
AWS_SECRET_KEY = 'Your secret key'
conn = boto.s3.connection.S3Connection(AWS_ACCESS_KEY, AWS_SECRET_KEY)
bucket = boto.s3.bucket.Bucket(conn, SRC_BUCKET_NAME)
for item in bucket:
# Note: here you can put also a path inside the DEST_BUCKET_NAME,
# if you want your item to be stored inside a folder, like this:
# bucket.copy(DEST_BUCKET_NAME, '%s/%s' % (folder_name, item.key))
bucket.copy(DEST_BUCKET_NAME, item.key)
Thanks - I use a slightly modified version, where I only copy files that don't exist or are a different size, and check on the destination if the key exists in the source. I found this a bit quicker for readying the test environment:
def botoSyncPath(path):
"""
Sync keys in specified path from source bucket to target bucket.
"""
try:
conn = S3Connection(AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY)
srcBucket = conn.get_bucket(AWS_SRC_BUCKET)
destBucket = conn.get_bucket(AWS_DEST_BUCKET)
for key in srcBucket.list(path):
destKey = destBucket.get_key(key.name)
if not destKey or destKey.size != key.size:
key.copy(AWS_DEST_BUCKET, key.name)
for key in destBucket.list(path):
srcKey = srcBucket.get_key(key.name)
if not srcKey:
key.delete()
except:
return False
return True
I wrote a script that backs up an S3 bucket: https://github.com/roseperrone/aws-backup-rake-task
#!/usr/bin/env python
from boto.s3.connection import S3Connection
import re
import datetime
import sys
import time
def main():
s3_ID = sys.argv[1]
s3_key = sys.argv[2]
src_bucket_name = sys.argv[3]
num_backup_buckets = sys.argv[4]
connection = S3Connection(s3_ID, s3_key)
delete_oldest_backup_buckets(connection, num_backup_buckets)
backup(connection, src_bucket_name)
def delete_oldest_backup_buckets(connection, num_backup_buckets):
"""Deletes the oldest backup buckets such that only the newest NUM_BACKUP_BUCKETS - 1 buckets remain."""
buckets = connection.get_all_buckets() # returns a list of bucket objects
num_buckets = len(buckets)
backup_bucket_names = []
for bucket in buckets:
if (re.search('backup-' + r'\d{4}-\d{2}-\d{2}' , bucket.name)):
backup_bucket_names.append(bucket.name)
backup_bucket_names.sort(key=lambda x: datetime.datetime.strptime(x[len('backup-'):17], '%Y-%m-%d').date())
# The buckets are sorted latest to earliest, so we want to keep the last NUM_BACKUP_BUCKETS - 1
delete = len(backup_bucket_names) - (int(num_backup_buckets) - 1)
if delete <= 0:
return
for i in range(0, delete):
print 'Deleting the backup bucket, ' + backup_bucket_names[i]
connection.delete_bucket(backup_bucket_names[i])
def backup(connection, src_bucket_name):
now = datetime.datetime.now()
# the month and day must be zero-filled
new_backup_bucket_name = 'backup-' + str('%02d' % now.year) + '-' + str('%02d' % now.month) + '-' + str(now.day);
print "Creating new bucket " + new_backup_bucket_name
new_backup_bucket = connection.create_bucket(new_backup_bucket_name)
copy_bucket(src_bucket_name, new_backup_bucket_name, connection)
def copy_bucket(src_bucket_name, dst_bucket_name, connection, maximum_keys = 100):
src_bucket = connection.get_bucket(src_bucket_name);
dst_bucket = connection.get_bucket(dst_bucket_name);
result_marker = ''
while True:
keys = src_bucket.get_all_keys(max_keys = maximum_keys, marker = result_marker)
for k in keys:
print 'Copying ' + k.key + ' from ' + src_bucket_name + ' to ' + dst_bucket_name
t0 = time.clock()
dst_bucket.copy_key(k.key, src_bucket_name, k.key)
print time.clock() - t0, ' seconds'
if len(keys) < maximum_keys:
print 'Done backing up.'
break
result_marker = keys[maximum_keys - 1].key
if __name__ =='__main__':main()
I use this in a rake task (for a Rails app):
desc "Back up a file onto S3"
task :backup do
S3ID = "*****"
S3KEY = "*****"
SRCBUCKET = "primary-mzgd"
NUM_BACKUP_BUCKETS = 2
Dir.chdir("#{Rails.root}/lib/tasks")
system "./do_backup.py #{S3ID} #{S3KEY} #{SRCBUCKET} #{NUM_BACKUP_BUCKETS}"
end
mdahlman's code didn't work for me but this command copies all the files in the bucket1 to a new folder (command also creates this new folder) in bucket 2.
cp --recursive --include=file_prefix* s3://bucket1/ s3://bucket2/new_folder_name/
s3cmd won't cp with only prefixes or wildcards but you can script the behavior with 's3cmd ls sourceBucket', and awk to extract the object name. Then use 's3cmd cp sourceBucket/name destBucket' to copy each object name in the list.
I use these batch files in a DOS box on Windows:
s3list.bat
s3cmd ls %1 | gawk "/s3/{ print \"\\"\"\"substr($0,index($0,\"s3://\"))\"\\"\"\"; }"
s3copy.bat
#for /F "delims=" %%s in ('s3list %1') do #s3cmd cp %%s %2
You can also use s3funnel which uses multi-threading:
https://github.com/neelakanta/s3funnel
example (without the access key or secret key parameters shown):
s3funnel source-bucket-name list | s3funnel dest-bucket-name copy --source-bucket source-bucket-name --threads=10