Posts Tagged ‘emr’

Using AWS Athena to query S3 Server Access Logs.

Written by mannem on . Posted in EMR || Elastic Map Reduce

S3 server access logs can grow very big over time and it is very hard for a single machine to Process/Query/Analyze all these logs. So, we can use distributed computing to query the logs quickly. Ideally we might think of Apache Hadoop/Hive/Spark/Presto etc to process these logs. But, the simplicity of AWS Athena service as a Serverless model will make it even easier. This article will guide you to use Athena to process your s3 access logs with example queries and has some partitioning considerations which can help you to query TB’s of logs just in few seconds.

The server access log files consist of a sequence of new-line delimited log records. Each log record represents one request and consists of space delimited fields. The following is an example log consisting of six log records.

This article is based on the Log format of access logs specified here The field’s and its format may change and DDL QUERY should be changed accordingly.

Sample Data:

Data Format considerations:

On first look, the data format appears simple , which is a textfile with space filed delimiter and newline(/n) delimited. However, there is a catch in this data format, the columns like Time , RequestURI & User-Agent can have space in their data ( [06/Feb/2014:00:00:38 +0000] , "GET /gdelt/1980.csv.gz HTTP/1.1" & aws-cli/1.7.36 Python/2.7.9 Linux/3.14.44-32.39.amzn1.x86_64) which will mess up with the SerDe parsing .

For RequestURI & User-Agent field, the data is enclosed in quotes with spaces inside it. This can be parsed by any SerDe’s that support Quotes. Unfortunately, Athena does not support such SerDe’s like org.apache.hadoop.hive.serde2.OpenCSVSerde which does has quotes feature.

The org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe included by Athena will not support quotes yet.

A custom SerDe called comes with all EMR AMI’s just for parsing these logs. A query like the following would create the table easily. However, this SerDe will not be supported by Athena.

Looking at these limitations, the org.apache.hadoop.hive.serde2.RegexSerDe only seems like the feasible option. Writing RegEx for your log structure was bit time consuming , but I was able to write it with the help of ELB log structure example and .

I used the following Regex for the S3 server access logs :

([^ ]*) ([^ ]*) \\[(.*?)\\] ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) ([^ ]*) \\\"([^ ]*) ([^ ]*) (- |[^ ]*)\\\" (-|[0-9]*) (-|[0-9]*) ([-0-9]*) ([-0-9]*) ([-0-9]*) ([-0-9]*) ([^ ]*) (\"[^\"]*\") ([^ ]*)$

Also found at : , where I removed some extra escape chars like \’s. In DDL , we need to add these escape characters like in following DDL.

([^ ]*) – matches a continuous string. Because the number fields like ‘time’ can return ‘-‘ character , I did not use ([-0-9]*) regex which is used for numbers. Because of same reason , I had to use STRING for all fields in DDL. We can use presto string functions to convert strings for appropriate conversions and comparison operators on DML

\\[(.*?)\\] or \\\[(.*?)\\\] – is for Time field – For a string like [06/Feb/2014:00:00:38 +0000], it will match and give a string like 05/Dec/2016:16:56:36 +0000 which is easier for querying between times.

\\\"([^ ]*) ([^ ]*) (- |[^ ]*)\\\" – is for Request-URI field – removes the quotes and splits the "GET /mybucket?versioning HTTP/1.1" into three groups.

(\"[^\"]*\") – is for User-Agent – matches the whole string with quotes.

(-|[0-9]*) – is for HTTPstatus which is always a number like 200 or 404 . If you expect a ‘-‘ string, you can use ([^ ]*) instead,


Where s3://s3server-accesslogs-bucket/logs/ is the bucket and prefix of you access logs.

Once you create the table, you can query this table for specific information :

Example Queries :

Any field can be set to “-” to indicate that the data was unknown or unavailable, or that the field was not applicable to this request.

Caveats :

1. From time to time, AWS might extend the access log record format by adding new fields to the end of each line.The DDL/RegEx must be written to handle trailing fields that it does not understand.

Partitioning considerations :

Overtime, the logs in your target prefix(s3://s3server-accesslogs-bucket/logs/) can grow big. This is especially true if you have this same bucket/prefix for other buckets access logs. If you enable logging on multiple source buckets that identify the same target bucket, the target bucket will have access logs for all those source buckets, but each log object will report access log records for a specific source bucket.

Amazon S3 uses the following object key format for the log objects it uploads in the target bucket: TargetPrefixYYYY-mm-DD-HH-MM-SS-UniqueString . So, it basically creates multiple files with a bunch of logs in the S3 bucket.

Querying can be slower if there are large number of small files in textformat.

To speed things up, we need at-least one of these options to consolidate these logs.
1. PARTITIONING – can work on subset of files instead of going through all files.
2. CONVERT TO a COLUMNAR STORAGE – like ORC or PARQUET For faster query performance

Unfortunately, we cannot do this by just defining a DDL. We need to copy/move out text based data-set with the above options enabled. One option to use of AWS EMR to periodically structure and partition the S3 access logs so that you can query those logs easily with Athena. At this moment, it is not possible to use Athena itself to convert non-partitioned data into partitioned data.

Here’s a example to convert Non partitioned s3 access logs to partitioned s3 access logs :

Continuing the Partitioning for incoming logs :

Note that above discussion talks about one time creation of all partitions. When you want to automatically add partitions to Athena with new files and coming to your S3 access logs with new request days on your log bucket, you will need to have a ‘batch job on schedule’ or a ‘trigger mechanism’ to do this.

Batch job on schedule :

Note that our source s3 access logs are dumped into one single directory. Each file name is like 2016-12-05-17-17-06-5D51435906E10586. Since we run our batch jobs after the above one-time-bulk-job, on the daily(or frequent) batches, we need to avoid the files that we already processed by previous one-time-job. For this , we can rely on the filename itself to get a list of newly generated files and then use an ETL to convert these files to partitioned data. We can also maintain a manifest file for the list of files already processed and the files which need to be processed etc. Managing all that is left beyond this blog. There’s multiple ways to do ETL. One way is to run an AWS Data Pipeline with EMR, on schedule, and run the hive queries on the new data.


Trigger based solution :

If you run a partition job on every S3 PUT or bunch of PUTS , you can use AWS Lambda which can trigger a piece of code on every S3 object PUT. You can get Metadata about that put and Handle that on your Lambda code accordingly to fireoff an ETL etc to do the partitioning. AWS Lambda should come with AWS SDK and you make all types of AWS API calls with proper permissions.

Other Input Regex’s :

'^(?!#)([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+[^\(]+[\(]([^\;]+).*\%20([^\/]+)[\/](.*)$'
ELB access logs
'([^ ]*) ([^ ]*) ([^ ]*):([0-9]*) ([^ ]*):([0-9]*) ([.0-9]*) ([.0-9]*) ([.0-9]*) (-|[0-9]*) (-|[0-9]*) ([-0-9]*) ([-0-9]*) \\\”([^ ]*) ([^ ]*) (- |[^ ]*)\\\” ?(\”[^\”]*\”)? ?([A-Z0-9-]+)? ?([A-Za-z0-9.-]*)?$'
Apache web logs
'([^ ]*) ([^ ]*) ([^ ]*) (-|\\[[^\\]]*\\]) ([^ \"]*|\"[^\"]*\") (-|[0-9]*) (-|[0-9]*)(?: ([^ \"]*|\"[^\"]*\") ([^ \"]*|\"[^\"]*\"))?'

You may also like:
Using Athena to Query CloudTrail logs.
Parsing AWS Cloudtrail logs with EMR Hive and Spark

EMR s3DistCp “–groupBy” Regex examples

Written by mannem on . Posted in EMR || Elastic Map Reduce

Sometimes the regex could be confusing on S3DistCp groupBy. I usually use some online regex tools like to better work with string matching and grouping.

Here are some examples that I explored so far :

Example 1 :

Example 2 :

s3-dist-cp –src s3://support.elasticmapreduce/training/datasets/gdelt/ –dest hdfs:///gdeltWrongOutput1/ –groupBy ‘.*(\d{6}).*’

This command would not merge any files but copy all files with 6 numbers like 20130401.export.CSV.gz to destination.

Example 3 :

Example 4 :

If you want to concatenate matching files in the root directory and and all matching files inside a ‘sample_directory’ into a single file and compress that in gzip format. on will concatenate all matched file contents and creates one .gz file

Parsing AWS Cloudtrail logs with EMR Hive and Spark

Written by mannem on . Posted in cloudtrail, EMR || Elastic Map Reduce


AWS CloudTrail is a web service that records AWS API calls for your account and delivers log files to you. The recorded information includes the identity of the API caller, the time of the API call, the source IP address of the API caller, the request parameters, and the response elements returned by the AWS service.

Cloudtrail usually sends the logs to S3 bucket segregated into Accounts(Regions(data(logFile.json.gz))).

Cloudtrail recommends to use aws-cloudtrail-processing-library , but it may be complex if you wish to Ad-hoc query a huge number of big log files faster. If there are many files , it may also be harder to download all logs to a Linux/Unix node , unzip it and do RegEx matching on all these files. So, we can use a distributed environment like Apache Hive on AWS EMR cluster to parse/organize all these files using very simple SQL like commands.

This article guides you to query your Cloudtrail logs using EMR Hive. This article also provides some example queries which may be useful in different scenarios. It assumes that you have a running EMR cluster which Hive application installed and explored a bit.

Directory and File structure of Cloudtrail logs :

The following Hive queries shows how to create a Hive table and reference the cloud trial s3 bucket. Cloudtrail data is processed by CloudTrailInputFormat implementation, which defines the input data split and key/value records. The CloudTrailLogDeserializer class defined in SerDe is called to format the data into a record that maps to column and data types in a table. Data (such as using an INSERT statement) to be written is translated by the Serializer class defined in SerDe to the format that the OUTPUTFORMAT class( HiveIgnoreKeyTextOutputFormat) can read.

These classes are part of /usr/share/aws/emr/goodies/lib/ EmrHadoopGoodies-x.jar & /usr/share/aws/emr/goodies/lib/ EmrHadoopGoodies-x.jar files and are automatically included in Hive classpath. Hive can also automatically de-compress the GZ files. All you need to do is to run a query similar to SQL commands. Some sample queries are included.

EMR uses an instance profile role on its nodes to auntenticate requests made to cloudtrail bucket. The default IAM policy on the role EMR_EC2_DefaultRole allows access to all S3 buckets. If your cluster do not have access , you may need to make sure the instance profile/Role has access to necessary s3 cloudtrail bucket.
Do not run any Insert overwrite on this hive table. If EMR has write access to the s3 bucket, an insert overwrite may delete all logs from this bucket. Please check hive language manual before attempting any commands
Cloudtrail json elements are extensive and are different for different kind of request. This SerDe (which is kind-of abandoned by EMR team)doesn’t include all possible rows in Ctrail. For example, if you try to query requestparameters , it would give FAILED: SemanticException [Error 10004]: Line 6:28 Invalid table alias or column reference ‘requestparameters’.
If your cloudtrail bucket has large number of files, Tez’s grouped splits or MR’s input splits calculation may take considerable time and memory. Make sure you allocate enough resources to the hive-client or tez-client.


Alternatively, you can use APACHE SPARK which has spark-shell to query cloutrail logs using the following library :

Here’s some instructions on this design :

hive server 2 crashing with OutOfMemoryError (OOM) ?

Written by mannem on . Posted in EMR || Elastic Map Reduce

emr ear  

Often times HiveServer2 can be single point of failure. It can easy crash with OOM. If HiveServer2 restarts now-and-then , it must be due to OOM where it is likely set to be killed and re-spawned. We need to check the JVM options to see the behavior of this process on OOM , analyze thread dumps and check the underlying issue to mitigate such issues.

This post explains – How to identify OOM issue on HiveServer2 – How to analyze Thread/Heap dumps – How to Mitigate this issue. Some of the discussion is generic to EMR.

PART 1 : How to identify OOM issue on HiveServer2

Default JVM options of HS2 on EMR 4.7.2 AMI :

Some observations from above JVM:

1. Heap space for HiveServer2 JVM is 1000MB and this option is sourced from
2. Hive logging options like(-Dhive.log.dir=/var/log/hive -Dhive.log.file=hive-server2.log -Dhive.log.threshold=INFO -server) are sourced from /etc/init/hive-server2.conf
3. All other options starting from ‘-server’ are set by EMR.
4. If this JVM goes OutOfMemoryError , -XX:OnOutOfMemoryError=kill -9 %p option will kill HS2 on OOM.

If for some reason the HiveServer2 goes OOM , it simply gets killed and there is no way to find why.. hive-server2.log and hive-server2.out may not show OOM at all. you can check hive-server2.log to verify HiveServer2 getting restarted . So, we tweak JVM options for HS2 to include GC verbose and to Dump Heap On OutOfMemoryError.

With the following settings , if the HS2 process does go OOM , it would display the error like following on /var/log/hive/hive-server2.out. This message means that for some reason the garbage collector of this HS2 JVM is taking an excessive amount of time and recovers very little memory in each run.

An example script to enable GC verbose on HS2 JVM – can be like following on EMR 4.x.x and 5.x.x:

PART 2 : How to analyze Thread/Heap dumps

With above options if HS2 fails with OOM , it will log such error to /var/log/hive/hive-server2.out. It will also heap dump to a file like /var/log/hive/java_pid8548.hprof . There are multiple ways to open and analyze this file. I found “eclipse memory analyzer tool” helpful in identifying memory leaks and exploring thread dumps on java classes and sub-classes.



PART 3 : How to Mitigate this issue

1. Tweaking hive-site.xml by checking stack trace:

Best way to mitigate the OOM’s is to check the stack trace of the Hive-server2 process and see if there’s any leaks. It is also a good idea to check for top consumers and act accordingly. For each of those threads , see if any of the hive-site.xml settings would reduce its memory consumption. Some times you may not have control on any of the thread specifics , in which case , you may need to further increase the heap space.

For example : In the stack trace ,

– if you see multiple threads getting blocked etc, you can edit a setting like hive.server2.thrift.http.max.worker.threads

2. Mitigating OOM obviously involves increasing the Heap space for Hive-server2.

The heap space is defined in using env variable . Its important to identify memory requirements of 3 hive services HiveServer2 , Hive metastore , Hive clients in advance , using load/concurrency tests before moving to PROD. If you observe any OOM’s , you may need to increase memory accordingly.

Identifying hive services and increasing memory :

if ‘export HADOOP_HEAPSIZE=2048’ string is present on , it would be applied to all of the 3 hive services if restarted. So, you can use if-statements to provide different settings(HADOOP_HEAPSIZE and HADOOP_OPTS) for 3 of these hive services.

Example contents of :

3. High availability and LoadBalancing of Hiveserver2 :

Increasing heap space for HS2 may help , However , As per Cloudera , after certain memory limit, there is possibility that you continue to hit OOM no mater how much you increase the memory for HS2. In that case, “Cloudera recommends splitting HiveServer2 into multiple instances and load balancing once you start allocating >12 GB to HiveServer2. The objective is to size to reduce impact of Java garbage collection on active processing by the service.”

You may checkout the limitations here :

4. Watch out for any bugs in the LockManager :

If you are using Hive’s Table Lock Manager Service by setting to true , check if there’s issues with the Lockmanager which is responsible for maintaining locks for concurrent user support. Lockmanager can be ZooKeeperHiveLockManager based or can be EmbeddedLockManager which is shared lock manager for dedicated hive server , where – all locks are managed in memory. if the issue seems to be with these lock managers , you may need to edit their configs.

5. Check for generic version issues :

On Apache JIRA issues for hive-server2.

6. GC Tuning :

If the HS2 JVM is spending too much time on GC , you might consider some GC Tuning as discussed here :

spark-redshift library from databricks – Installation on EMR and using InstanceProfile

Written by mannem on . Posted in EMR || Elastic Map Reduce

ear earearspark-redshift is a library to load data into Spark SQL DataFrames from Amazon Redshift, and write them back to Redshift tables. Amazon S3 is used to efficiently transfer data in and out of Redshift, and JDBC is used to automatically trigger the appropriate COPY and UNLOAD commands on Redshift.This library is more suited to ETL than interactive queries, since large amounts of data could be extracted to S3 for each query execution

spark-redshift installation instructions on EMR:
Steps 1-8 shows how to compile your own spark-redshift package(JAR). You can directly skip to step 9 if you wish to use pre-compiled JAR’s(V 0.6.1). Later we use spark-shell to invoke these JAR’s and run scala code to query Redshift table and put contents into a dataframe. This guide assumes you had followed github page and its tutorial.

Download pre-req’s to compile the your own JAR’s


If you wanna skip above steps , you can download the pre-compiled JAR’s using –



With these JAR’s you can skip all above options (1-8)


10. In SCALA shell, you can run the following commands to init SQLContext. Note that the below code automatically uses IAM role’s (Instance profile ) cred’s to authenticate against S3

Sample Scala code uses Instance profile


Sample spark-sql invocation

spark-sql --jars RedshiftJDBC41-,spark-redshift_2.10-0.6.1-SNAPSHOT.jar,minimal-json-0.9.5-SNAPSHOT.jar

Note that while running the above commands , the spark-redshift executes a COPY/Unload with a command like below:

These temporary credentials are from IAM role’s (EMR_EC2_DefaultRole) and this role should have policy that allows S3 access to atleast temp bucket mentioned in the command.

hbase snapshot / export

Written by mannem on . Posted in EMR || Elastic Map Reduce

I observed that exporting large Hbase tables with Hbase provided ‘Export’ utility is very high CPU bound. If you are using default cluster configurations, the mappers may consume 100% CPU and may crash the regionServer(core-node) and your hbase. This article discusses some tuning on your map/reduce hbase export job. Also it focus on an alternate Hbase utility called ‘ExportSnapshot’ which mitigates problems with Hbase Export utility.

This article touches import/export tools that ship with hbase and shows how to use them efficiently

A small intro on Hbase Utilities.


Export is a utility that will dump the contents of table to HDFS in a sequence file.


CopyTable is a utility that can copy part or of all of a table, either to the same cluster or another cluster. The target table must first exist. The usage is as follows:


The ExportSnapshot tool copies all the data related to a snapshot (hfiles, logs, snapshot metadata) to another cluster. The tool executes a Map-Reduce job, similar to distcp, to copy files between the two clusters, and since it works at file-system level the hbase cluster does not have to be online.

The main difference between Exporting a Snapshot and Copying/Exporting a table is that ExportSnapshot operates at HDFS level.

This means that Master and Region Servers are not involved in this operations.

Consequently, no unnecessary caches for data are created and there is no triggering of additional GC pauses due to the number of objects created during the scan process.

So, when Exporting snapshot, the job is not longer CPU bound.

Please see :

  • Using snapshots, i was able to Export my hbase table from Source cluster with EMR 3.0.0 AMI to Target cluster with latest(at the time of writing this post) EMR 3.9.0 AMI.

    This job was considerably very fast than Hbase Export.

    Prepping source and target clusters:


    —Enabling snapshots on Hbase—

    You must add the following property to hbase-site.xml of HBase Master node:
    ‘hbase.snapshot.enabled’ property with value ‘true’.


    Stop the HMaster process.

    > sudo service hbase-master stop

    service-nanny will restart this process again. This will load the new configuration.

    —Enabling communication between source and Target clusters—

    To transfer snapshot from source cluster to target cluster,

    The source cluster need to communicate with target node’s name-node process listening on hdfs default port 9000(hdfs getconf -confKey

    if both these clusters are using default EMR security groups, then they can communicate by default.Otherwise you may need to enable the security groups to allow communication over that port.

    (Check telnet 9000 from source cluster’s master)


    —Creating a snapshot on Source cluster—

    On source cluster(EMR 3.0.0 )

    > hbase shell
    hbase(main):001:0> snapshot 'sourceTable', 'snapshotName'

    check if the snapshot is created at HDFS /hbase/.hbase-snapshot/

    —Exporting this snapshot(snapshotName) to Hdfs /hbase/.hbase-snapshot/ of Target cluster(EMR 3.9.0)—

    with Master node’s internal IP
    listening on HDFS port 9000.
    Using 16 mappers
    with ‘ExportSnapshot’

    > hbase org.apache.hadoop.hbase.snapshot.ExportSnapshot -snapshot snapshotName -copy-to hdfs:// -mappers 16


    On Target cluster, check if the snapshot is exported

    > hadoop fs -ls /hbase/.hbase-snapshot/

    —Clone this snapshot to actual Hbase table on Target cluster—

    > hbase shell
    hbase(main):001:0> clone_snapshot 'snapshotName', 'newTableName'


    Installing and using Apache sqoop to export/Import large datasets (MySQL, S3) (CSV/TSV..) on EMR cluster

    Written by mannem on . Posted in EMR || Elastic Map Reduce


    Often times the export/import activity may be limited on several performance bottlenecks. So, the activity may be faster if a distributed transfer is used instead of normal transfer. Some of the bottlenecks include Read Throughput , Write throughput , how the code parses the data(Inline or Batch etc,. Apache Sqoop(TM) is a tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases.

    This guide shows you
    > To Install sqoop and
    > Export/Import MySQL tables (from S3 to RDS) ,(from RDS to S3) respectively.

    Considering a sample MySQL Table in RDS

    > mysql -h -u mannem -p

    mysql> describe dailyStockPrices;

    mysql> SELECT table_name AS "Table",
    -> round(((data_length + index_length) / 1024 / 1024), 2) "Size in MB"
    -> FROM information_schema.TABLES
    -> WHERE table_schema = "pipetest"
    -> AND table_name = "dailyStockPrices";

    > My MySQL table dataset has lot of commas in the fields, so I choose TSV format instead of CSV to import/export.
    > If I used CSV format, Sqoop will get confused parsing data.

    Sqoop on EMR 4.4.0 + is pre-installed

    Starting from EMR AMI version 4.4.0 , Sqoop 1.4.6 is available as sandbox. This can be installed by simply selecting this option while provisioning the EMR cluster. By default, Sqoop on EMR has a MariaDB and PostgresSQL driver installed. To install an alternate set of JDBC connectors for Sqoop, you need to install them in /usr/lib/sqoop/lib.

    Sqoop on EMR 3.x.x can be installed with the following script:

    Import Commands:

    Import sqoop
    This command copies MySQL table from RDS to S3. The S3 file content type is TSV & File name will be in “part-m-00000” format.
    Note that with -m 1 , I am using single mapper task to run in parallel.
    sqoop import --connect jdbc:mysql:// --username mannem --password Password123 --table dailyStockPrices --target-dir s3://mannem/sqoopmatrix -m 1 --fields-terminated-by '\t' --lines-terminated-by '\n'
    Check S3 contents
    hadoop fs -cat s3://mannem/sqoopmatrix/part-m-00000
    Sqoop command usage:

    Export commands :

    Before export, The Destination MySQL/PSQL table should already be created with a similar schema.
    Export to RDS(MySQL)
    This command copies TSV file from S3 to MySQL Table.
    sqoop export --connect jdbc:mysql:// --username mannem --password Password123 --table dailyStockPrices --input-fields-terminated-by '\t' --input-lines-terminated-by '\n' --export-dir s3://mannem/sqoopmatrix/part-m-00000
    Export to Redshift(PSQL)
    sqoop export --connect jdbc:redshift://$MYREDSHIFTHOST:5439/mydb --table mysqoopexport --export-dir s3://mybucket/myinputfiles/ --driver --username master --password Mymasterpass1
    Export commands with mariadb connection string
    sqoop export --connect jdbc:mariadb://$HOSTNAME:3306/mydb --table mysqoopexport --export-dir s3://mybucket/myinputfiles/ --driver org.mariadb.jdbc.Driver --username master --password Mymasterpass1
    Export with using Secure Socket Layer encryption
    sqoop export --connect jdbc:mariadb://$HOSTNAME:3306/mydb?verifyServerCertificate=false&useSSL=true&requireSSL=true --table mysqoopexport --export-dir s3://mybucket/myinputfiles/ --driver org.mariadb.jdbc.Driver --username master --password Mymasterpass1


    Written by mannem on . Posted in Achitectures


    Amazon Web Services provides services and infrastructure to build reliable, fault-tolerant, and highly available web applications in the cloud. In production environments, these applications can generate huge amounts of log information. This data can be an important source of knowledge for any company that is operating web applications. Analyzing logs can reveal information such as traffic patterns, user behavior, marketing profiles, etc.

    However, as the web application grows and the number of visitors increases, storing and analyzing web logs becomes increasingly challenging. This diagram shows how to use Amazon Web Services to build a scalable and reliable large-scale log analytics platform. The core component of this architecture is Amazon Elastic MapReduce, a web service that enables analysts to process large amounts of data easily and cost-effectively using a Hadoop hosted framework.


    Written by mannem on . Posted in Achitectures


    When data arrives as a succession of regular measurements, it is known as time series information. Processing of time series information poses systems scaling challenges that the elasticity of AWS services is uniquely positioned to address.
    This elasticity is achieved by using Auto Scaling groups for ingest processing, AWS Data Pipeline for scheduled Amazon Elastic MapReduce jobs, AWS Data Pipeline for intersystem data orchestration, and Amazon Redshift for potentially massive-scale analysis. Key architectural throttle points involving Amazon SQS for sensor message buffering and less frequent AWS Data Pipeline scheduling keep the overall solution costs predictable and controlled.


    Written by mannem on . Posted in Achitectures


    Financial services grid computing on the cloud provides dynamic scalability and elasticity for operation when compute jobs are required, and utilizing services for aggregation that simplify the
    development of grid software.

    On demand provisioning of hardware, and template driven deployment, combined with low latency access to existing onpremise data sources make AWS a powerful platform for high performance grid computing systems.

    • cloudformation












    • snap





      Storage Gatewa




      Cloud Front

    • r53

      Route 53











    • sns







      Cloud Search


      App Stream



    • opsworks



      Cloud Watch


      Elastic Beanstalk


      Code Deploy



    • dynamodb