Reflow is a system for incremental data processing in the cloud. Reflow enables scientists and engineers to compose existing tools (packaged in Docker images) using ordinary programming constructs. Reflow then evaluates these programs in a cloud environment, transparently parallelizing work and memoizing results. Reflow was created at GRAIL to manage our NGS (next generation sequencing) bioinformatics workloads on AWS, but has also been used for many other applications, including model training and ad-hoc data analyses.

Reflow comprises:

• a functional, lazy, type-safe domain specific language for writing workflow programs;
• a runtime for evaluating Reflow programs incrementally, coordinating cluster execution, and transparent memoization;
• a cluster scheduler to dynamically provision and tear down resources from a cloud provider (AWS currently supported).

Reflow thus allows scientists and engineers to write straightforward programs and then have them transparently executed in a cloud environment. Programs are automatically parallelized and distributed across multiple machines, and redundant computations (even across runs and users) are eliminated by its memoization cache. Reflow evaluates its programs incrementally: whenever the input data or program changes, only those outputs that depend on the changed data or code are recomputed.

In addition to the default cluster computing mode, Reflow programs can also be run locally, making use of the local machine's Docker daemon (including Docker for Mac).

Reflow was designed to support sophisticated, large-scale bioinformatics workflows, but should be widely applicable to scientific and engineering computing workloads. It was built using Go.

Reflow joins a long list of systems designed to tackle bioinformatics workloads, but differ from these in important ways:

• it is a vertically integrated system with a minimal set of external dependencies; this allows Reflow to be "plug-and-play": bring your cloud credentials, and you're off to the races;
• it defines a strict data model which is used for transparent memoization and other optimizations;
• it takes workflow software seriously: the Reflow DSL provides type checking, modularity, and other constructors that are commonplace in general purpose programming languages.

Getting Reflow

You can get binaries (macOS/amd64, Linux/amd64) for the latest release at the GitHub release page.

Reflow is implemented in Go, and its packages are go-gettable. You can retrieve Reflow and its dependencies with

% go get [-u] github.com/grailbio/reflow

and build the "reflow" binary using

% go install github.com/grailbio/reflow/cmd/reflow

Note that Reflow makes use of its own agent binaries, called reflowlets. These are compiled for Linux/amd64, and are invoked by the cluster manager during instance bootstrapping. Thus, when you make changes to the code, you may also have to update the agent docker image. This process is handled by command releasereflow, which can be installed by

% go install github.com/grailbio/reflow/cmd/releasereflow

Releasereflow builds target agent binaries and uploads them to a Docker repository (the release builds use the grailbio/reflowlet repository). Releasereflow then updates the file version.go which is compiled into the standard reflow binary.

Quickstart - AWS

Reflow is distributed with an EC2 cluster manager, and a memoization cache implementation based on S3. These must be configured before use. Reflow maintains a configuration file in $HOME/.reflow/config.yaml by default (this can be overridden with the -config option). Reflow's setup commands modify this file directly. After each step, the current configuration can be examined by running reflow config.

Note Reflow must have access to AWS credentials in the environment (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) while running these commands.

% reflow setup-ec2
% reflow config
cluster: ec2cluster
ec2cluster:
  ami: ami-d0e54eb0
  diskspace: 100
  disktype: gp2
  instancetypes:
  - c1.medium
  - c1.xlarge
  - c3.2xlarge
  - c3.4xlarge
  - c3.8xlarge
  - c3.large
  - c3.xlarge
  - c4.2xlarge
  - c4.4xlarge
  - c4.8xlarge
  - c4.large
  - c4.xlarge
  - cc2.8xlarge
  - m1.large
  - m1.medium
  - m1.small
  - m1.xlarge
  - m2.2xlarge
  - m2.4xlarge
  - m2.xlarge
  - m3.2xlarge
  - m3.large
  - m3.medium
  - m3.xlarge
  - m4.16xlarge
  - m4.4xlarge
  - m4.xlarge
  - r4.xlarge
  - t1.micro
  - t2.large
  - t2.medium
  - t2.micro
  - t2.nano
  - t2.small
  keyname: ""
  maxinstances: 10
  region: us-west-2
  securitygroup: <a newly created security group here>
  sshkey: <your public SSH key here>
https: httpsca,$HOME/.reflow/reflow.pem

After running reflow setup-ec2, we see that Reflow created a new security group (associated with the account's default VPC), and configured the cluster to use some default settings. Feel free to edit the configuration file ($HOME/.reflow/config.yaml) to your taste. If you want to use spot instances, add a new key under ec2cluster: spot: true.

Reflow only configures one security group per account: Reflow will reuse a previously created security group if reflow setup-ec2 is run anew. See reflow setup-ec2 -help for more details.

Next, we'll set up a cache. This isn't strictly necessary, but we'll need it in order to use many of Reflow's sophisticated caching and incremental computation features. On AWS, Reflow implements a cache based on S3 and DynamoDB. A new S3-based cache is provisioned by reflow setup-s3-cache. It takes two arguments. The first is the name of the S3 bucket to use. If you provide an existing S3 bucket, it is used, or else a new bucket is created. The second argument is the name of the DynamoDB table, where again existing tables are reused. Note that S3 bucket names are global, so pick a name that's likely to be unique.

% reflow setup-s3-cache reflow-quickstart-cache reflow-quickstart
2017/10/18 15:09:10 creating s3 bucket reflow-quickstart-cache
2017/10/18 15:09:12 created s3 bucket reflow-quickstart-cache
% reflow config
cache: s3,reflow-quickstart-cache,reflow-quickstart
<same as before>

Reflow setup-s3-cache creates the S3 bucket and DynamoDB table as needed, and modified the configuration accordingly.

We're now ready to run our first "hello world" program!

Create a file called "hello.rf" with the following contents:

val Main = exec(image := "ubuntu", mem := GiB) (out file) {"
	echo hello world >>{{out}}
"}

and run it:

% reflow run hello.rf
2017/10/18 15:11:05 run name: marius@localhost/e08374e8
2017/10/18 15:11:08 ec2cluster: launched instance i-0bd7d189617e53767: t2.small: 1.9GiB 1 100.0GiB
2017/10/18 15:11:54 -> hello.Main   3dca1cc0 run    exec ubuntu echo hello world >>{{out}}
2017/10/18 15:12:02 <- hello.Main   3dca1cc0 ok     exec 0s 12B
2017/10/18 15:12:02 total n=1 time=8s
	ident      n   ncache runtime(m) cpu mem(GiB) disk(GiB) tmp(GiB)
	hello.Main 1   0                                        
	
file(sha256=sha256:a948904f2f0f479b8f8197694b30184b0d2ed1c1cd2a1ec0fb85d299a192a447, size=12)

Here, Reflow started a new t2.small instance (Reflow matches the workload with available instance types), ran "echo hello world" inside of an Ubuntu container, placed the output in a file, and returned its SHA256 digest. (Reflow represents file contents using their SHA256 digest.)

We're now ready to explore Reflow more fully.

Simple bioinformatics workflow

Let's explore some of Reflow's features through a simple task: aligning NGS read data from the 1000genomes project. Create a file called "align.rf" with the following. The code is commented inline for clarity.

// In order to align raw NGS data, we first need to construct an index
// against which to perform the alignment. We're going to be using
// the BWA aligner, and so we'll need to retrieve a reference sequence
// and create an index that's usable from BWA.

// g1kv37 is the a human reference FASTA sequence. (All
// chromosomes.) Reflow has a static type system, but most type
// annotations can be omitted: they are inferred by Reflow. In this
// case, we're creating a file: a reference to the contents of the
// named URL. We're retrieving data from the public 1000genomes S3
// bucket.
val g1kv37 = file("s3://1000genomes/technical/reference/human_g1k_v37.fasta.gz")

// Here we create an indexed version of the g1kv37 reference. It is
// created using the "bwa index" command with the raw FASTA data as
// input. Here we encounter another way to produce data in reflow:
// the exec. An exec runs a (Bash) script inside of a Docker image,
// placing the output in files or directories (or both: execs can
// return multiple values). In this case, we're returning a
// directory since BWA stores multiple index files alongside the raw
// reference. We also declare that the image to be used is
// "biocontainers/bwa" (the BWA image maintained by the
// biocontainers project).
//
// Inside of an exec template (delimited by {" and "}) we refer to
// (interpolate) values in our environment by placing expressions
// inside of the {{ and }} delimiters. In this case we're referring
// to the file g1kv37 declared above, and our output, named out.
//
// Many types of expressions can be interpolated inside of an exec,
// for example strings, integers, files, and directories. Strings
// and integers are rendered using their normal representation,
// files and directories are materialized to a local path before
// starting execution. Thus, in this case, {{g1kv37}} is replaced at
// runtime by a path on disk with a file with the contents of the
// file g1kv37 (i..e,
// s3://1000genomes/technical/reference/human_g1k_v37.fasta.gz)
val reference = exec(image := "biocontainers/bwa", mem := GiB, cpu := 1) (out dir) {"
	# Ignore failures here. The file from 1000genomes has a trailer
	# that isn't recognized by gunzip. (This is not recommended practice!)
	gunzip -c {{g1kv37}} > {{out}}/g1k_v37.fa || true
	cd {{out}}
	bwa index -a bwtsw g1k_v37.fa
"}

// Now that we have defined a reference, we can define a function to
// align a pair of reads against the reference, producing an output
// SAM-formatted file. Functions compute expressions over a set of
// abstract parameters, in this case, a pair of read files. Unlike almost
// everywhere else in Reflow, function parameters must be explicitly
// typed.
//
// (Note that we're using a syntactic short-hand here: parameter lists can 
// be abbreviated. "r1, r2 file" is equivalent to "r1 file, r2 file".)
//
// The implementation of align is a straightforward invocation of "bwa mem".
// Note that "r1" and "r2" inside of the exec refer to the function arguments,
// thus align can be invoked for any set of r1, r2.
func align(r1, r2 file) = 
	exec(image := "biocontainers/bwa", mem := 20*GiB, cpu := 16) (out file) {"
		bwa mem -M -t 16 {{reference}}/g1k_v37.fa {{r1}} {{r2}} > {{out}}
	"}

// We're ready to test our workflow now. We pick an arbitrary read
// pair from the 1000genomes data set, and invoke align. There are a
// few things of note here. First is the identifier "Main". This
// names the expression that's evaluated by "reflow run" -- the
// entry point of the computation. Second, we've defined Main to be
// a block. A block is an expression that contains one or more
// definitions followed by an expression. The value a block is the
// final expression. Finally, Main contains a @requires annotation.
// This instructs Reflow how many resources to reserve for the work
// being done. Note that, because Reflow is able to distribute work,
// if a single instance is too small to execute fully in parallel,
// Reflow will provision additional compute instances to help along.
// @requires thus denotes the smallest possible instance
// configuration that's required for the program.
@requires(cpu := 16, mem := 24*GiB, disk := 50*GiB)	
val Main = {
	r1 := file("s3://1000genomes/phase3/data/HG00103/sequence_read/SRR062640_1.filt.fastq.gz")
	r2 := file("s3://1000genomes/phase3/data/HG00103/sequence_read/SRR062640_2.filt.fastq.gz")
	align(r1, r2)
}

Now we're ready to run our module. First, let's run reflow doc. This does two things. First, it typechecks the module (and any dependent modules), and second, it prints documentation for the public declarations in the module. Identifiers that begin with an uppercase letter are public (and may be used from other modules); others are not.

% reflow doc align.rf
Declarations

val Main (out file)
    We're ready to test our workflow now. We pick an arbitrary read pair from the
    1000genomes data set, and invoke align. There are a few things of note here.
    First is the identifier "Main". This names the expression that's evaluated by
    "reflow run" -- the entry point of the computation. Second, we've defined Main
    to be a block. A block is an expression that contains one or more definitions
    followed by an expression. The value a block is the final expression. Finally,
    Main contains a @requires annotation. This instructs Reflow how many resources
    to reserve for the work being done. Note that, because Reflow is able to
    distribute work, if a single instance is too small to execute fully in parallel,
    Reflow will provision additional compute instances to help along. @requires thus
    denotes the smallest possible instance configuration that's required for the
    program.

Then let's run it:

% reflow run align.rf
2017/10/18 15:47:51 run name: marius@localhost/fb77159e
2017/10/18 15:47:54 ec2cluster: launched instance i-0a6a6901865001c4c: c4.4xlarge: 28.5GiB 16 100.0GiB

Reflow launched a new instance: the previously launched instance (a t2.small) was not big enough to fit the requirements of align.rf. Note also that Reflow assigns a run name for each "reflow run" invocation. This can be used to look up run details with the "reflow info" command. In this case:

% reflow info marius@localhost/fb77159e
marius@localhost/fb77159e
    time:    Wed Oct 18 15:47:52 2017
    program: /Users/marius/align.rf
    phase:   Eval
    alloc:   ec2-34-210-106-90.us-west-2.compute.amazonaws.com:9000/a79b63ddf0952cff
    log:     /Users/marius/.reflow/runs/marius@localhost/fb77159efc639b14aaff55fcd5229484ec78694ada7973bd0af862fd4f06adbd.execlog

Here we see that the run is currently being performed on the alloc named ec2-34-210-106-90.us-west-2.compute.amazonaws.com:9000/a79b63ddf0952cff. An alloc is a resource reservation on a single machine. A run can make use of multiple allocs to distribute work across multiple machines. The alloc is a URI, and the first component is the real hostname. You can ssh into the host in order to inspect what's going on. Reflow launched the instance with your public SSH key (as long as it was setup by reflow setup-ec2, and $HOME/.ssh/id_rsa.pub existed at that time).

% ssh [email protected]
...

As the run progresses, Reflow prints execution status on the console. Lines beginning with "->" indicate that a task was started (e.g., download a file, start an exec), while lines beginning with "<-" indicate that a task finished.

...
2017/10/18 16:03:07 -> example.Main.r2 a8788913 run  intern ..hase3/data/HG00103/sequence_read/SRR062640_2.filt.fastq.gz
2017/10/18 16:03:07 -> example.Main.r1 882d8d39 run  intern ..hase3/data/HG00103/sequence_read/SRR062640_1.filt.fastq.gz
2017/10/18 16:03:14 -> example.reference a4a82ba4 run    exec biocontainers/bwa # Ignore failures here. The f..bwa index -a bwtsw g1k_v37.fa

Here, Reflow started downloading r1 and r2 in parallel with creating the reference. Creating the reference is an expensive operation. We can examine it while it's running with "reflow ps":

% reflow ps 
marius@localhost/220ab625 example.reference 4:03PM 0:00 running 272.8MiB 8.0 4.4GiB bwa

This tells us that the only task that's currently running is bwa to compute the reference. It's currently using 272MiB of memory, 8 cores, and 4.4 GiB of disk space. By passing the -l option, reflow ps also prints the task's exec URI.

% reflow ps -l
marius@localhost/220ab625 example.reference 4:03PM 0:00 running 302.8MiB 8.0 4.4GiB bwa ec2-52-11-236-67.us-west-2.compute.amazonaws.com:9000/6b2879b9c282a12f/a4a82ba4b5c6d82985d31e79be5a8fe568d75a86db284d78f9972df525cf70d3

An exec URI is a handle to the actual task being executed. It globally identifies all tasks, and can examined with "reflow info":

% reflow info ec2-52-11-236-67.us-west-2.compute.amazonaws.com:9000/6b2879b9c282a12f/a4a82ba4b5c6d82985d31e79be5a8fe568d75a86db284d78f9972df525cf70d3
ec2-52-11-236-67.us-west-2.compute.amazonaws.com:9000/6b2879b9c282a12f/a4a82ba4b5c6d82985d31e79be5a8fe568d75a86db284d78f9972df525cf70d3
    state: running
    type:  exec
    ident: example.reference
    image: biocontainers/bwa
    cmd:   "\n\t# Ignore failures here. The file from 1000genomes has a trailer\n\t# that isn't recognized by gunzip. (This is not recommended practice!)\n\tgunzip -c {{arg[0]}} > {{arg[1]}}/g1k_v37.fa || true\n\tcd {{arg[2]}}\n\tbwa index -a bwtsw g1k_v37.fa\n"
      arg[0]:
        .: sha256:8b6c538abf0dd92d3f3020f36cc1dd67ce004ffa421c2781205f1eb690bdb442 (851.0MiB)
      arg[1]: output 0
      arg[2]: output 0
    top:
         bwa index -a bwtsw g1k_v37.fa

Here, Reflow tells us that the currently running process is "bwa index...", its template command, and the SHA256 digest of its inputs. Programs often print helpful output to standard error while working; this output can be examined with "reflow logs":

% reflow logs ec2-52-11-236-67.us-west-2.compute.amazonaws.com:9000/6b2879b9c282a12f/a4a82ba4b5c6d82985d31e79be5a8fe568d75a86db284d78f9972df525cf70d3
gzip: /arg/0/0: decompression OK, trailing garbage ignored
[bwa_index] Pack FASTA... 22.36 sec
[bwa_index] Construct BWT for the packed sequence...
[BWTIncCreate] textLength=6203609478, availableWord=448508744
[BWTIncConstructFromPacked] 10 iterations done. 99999990 characters processed.
[BWTIncConstructFromPacked] 20 iterations done. 199999990 characters processed.
[BWTIncConstructFromPacked] 30 iterations done. 299999990 characters processed.
[BWTIncConstructFromPacked] 40 iterations done. 399999990 characters processed.
[BWTIncConstructFromPacked] 50 iterations done. 499999990 characters processed.
[BWTIncConstructFromPacked] 60 iterations done. 599999990 characters processed.
%

At this point, it looks like everything is running as expected. There's not much more to do than wait. Note that, while creating an index takes a long time, Reflow only has to compute it once. When it's done, Reflow memoizes the result, uploading the resulting data directly to the configured S3 cache bucket. The next time the reference expression is encountered, Reflow will use the previously computed result. If the input file changes (e.g., we decide to use another reference sequence), Reflow will recompute the index again. The same will happen if the command (or Docker image) that's used to compute the index changes. Reflow keeps track of all the dependencies for a particular sub computation, and recomputes them only when dependencies have changed. This way, we always know what being computed is correct (the result is the same as if we had computed the result from scratch), but avoid paying the cost of redundant computation.

After a little while, the reference will have finished generating, and Reflow begins alignment. Here, Reflow reports that the reference took 56 minutes to compute, and produced 8 GiB of output.

2017/10/19 10:04:39 <- align.reference a4a82ba4 ok     exec 56m49s 8.0GiB
2017/10/19 10:04:39 -> align.align  80b24fa0 run    exec biocontainers/bwa bwa mem -M -t 16 {{reference}..37.fa {{r1}} {{r2}} > {{out}}

If we query ("info") the reference exec again, Reflow reports precisely what was produced:

% reflow info ec2-34-215-254-146.us-west-2.compute.amazonaws.com:9000/e34ae0cf7c0482f6/a4a82ba4b5c6d82985d31e79be5a8fe568d75a86db284d78f9972df525cf70d3
ec2-34-215-254-146.us-west-2.compute.amazonaws.com:9000/e34ae0cf7c0482f6/a4a82ba4b5c6d82985d31e79be5a8fe568d75a86db284d78f9972df525cf70d3
    state: complete
    type:  exec
    ident: align.reference
    image: biocontainers/bwa
    cmd:   "\n\t# Ignore failures here. The file from 1000genomes has a trailer\n\t# that isn't recognized by gunzip. (This is not recommended practice!)\n\tgunzip -c {{arg[0]}} > {{arg[1]}}/g1k_v37.fa || true\n\tcd {{arg[2]}}\n\tbwa index -a bwtsw g1k_v37.fa\n"
      arg[0]:
        .: sha256:8b6c538abf0dd92d3f3020f36cc1dd67ce004ffa421c2781205f1eb690bdb442 (851.0MiB)
      arg[1]: output 0
      arg[2]: output 0
    result:
      list[0]:
        g1k_v37.fa:     sha256:2f9cd9e853a9284c53884e6a551b1c7284795dd053f255d630aeeb114d1fa81f (2.9GiB)
        g1k_v37.fa.amb: sha256:dd51a07041a470925c1ebba45c2f534af91d829f104ade8fc321095f65e7e206 (6.4KiB)
        g1k_v37.fa.ann: sha256:68928e712ef48af64c5b6a443f2d2b8517e392ae58b6a4ab7191ef7da3f7930e (6.7KiB)
        g1k_v37.fa.bwt: sha256:2aec938930b8a2681eb0dfbe4f865360b98b2b6212c1fb9f7991bc74f72d79d8 (2.9GiB)
        g1k_v37.fa.pac: sha256:d62039666da85d859a29ea24af55b3c8ffc61ddf02287af4d51b0647f863b94c (739.5MiB)
        g1k_v37.fa.sa:  sha256:99eb6ff6b54fba663c25e2642bb2a6c82921c931338a7144327c1e3ee99a4447 (1.4GiB)

In this case, "bwa index" produced a number of auxilliary index files. These are the contents of the "reference" directory.

We can again query Reflow for running execs, and examine the alignment. We see now that the reference is passed in (argument 0), along side the read pairs (arguments 1 and 2).

% reflow ps -l
marius@localhost/e22cbf4c align.align 10:04AM 0:00 running 7.8GiB 127.9 5.8GiB bwa ec2-34-215-254-146.us-west-2.compute.amazonaws.com:9000/e34ae0cf7c0482f6/80b24fa01b988ca666d4c1eae0fa21d1dbd68e9cd18a82fc4cd6da20fec5abbd
% reflow info ec2-34-215-254-146.us-west-2.compute.amazonaws.com:9000/e34ae0cf7c0482f6/80b24fa01b988ca666d4c1eae0fa21d1dbd68e9cd18a82fc4cd6da20fec5abbd
ec2-34-215-254-146.us-west-2.compute.amazonaws.com:9000/e34ae0cf7c0482f6/80b24fa01b988ca666d4c1eae0fa21d1dbd68e9cd18a82fc4cd6da20fec5abbd
    state: running
    type:  exec
    ident: align.align
    image: biocontainers/bwa
    cmd:   "\n\t\tbwa mem -M -t 16 {{arg[0]}}/g1k_v37.fa {{arg[1]}} {{arg[2]}} > {{arg[3]}}\n\t"
      arg[0]:
        g1k_v37.fa:     sha256:2f9cd9e853a9284c53884e6a551b1c7284795dd053f255d630aeeb114d1fa81f (2.9GiB)
        g1k_v37.fa.amb: sha256:dd51a07041a470925c1ebba45c2f534af91d829f104ade8fc321095f65e7e206 (6.4KiB)
        g1k_v37.fa.ann: sha256:68928e712ef48af64c5b6a443f2d2b8517e392ae58b6a4ab7191ef7da3f7930e (6.7KiB)
        g1k_v37.fa.bwt: sha256:2aec938930b8a2681eb0dfbe4f865360b98b2b6212c1fb9f7991bc74f72d79d8 (2.9GiB)
        g1k_v37.fa.pac: sha256:d62039666da85d859a29ea24af55b3c8ffc61ddf02287af4d51b0647f863b94c (739.5MiB)
        g1k_v37.fa.sa:  sha256:99eb6ff6b54fba663c25e2642bb2a6c82921c931338a7144327c1e3ee99a4447 (1.4GiB)
      arg[1]:
        .: sha256:0c1f85aa9470b24d46d9fc67ba074ca9695d53a0dee580ec8de8ed46ef347a85 (1.8GiB)
      arg[2]:
        .: sha256:47f5e749123d8dda92b82d5df8e32de85273989516f8e575d9838adca271f630 (1.7GiB)
      arg[3]: output 0
    top:
         /bin/bash -e -l -o pipefail -c ..bwa mem -M -t 16 /arg/0/0/g1k_v37.fa /arg/1/0 /arg/2/0 > /return/0 .
         bwa mem -M -t 16 /arg/0/0/g1k_v37.fa /arg/1/0 /arg/2/0
% 

Note that the read pairs are files. Files in Reflow do not have names; they are just blobs of data. When Reflow runs a process that requires input files, those anonymous files are materialized on disk, but the filenames are not meaningful. In this case, we can see from the "top" output (these are the actual running processes, as reported by the OS), that the r1 ended up being called "/arg/1/0" and r2 "/arg/2/0". The output is a file named "/return/0".

Finally, alignment is complete. Aligning a single read pair took around 19m, and produced 13.2 GiB of output. Upon completion, Reflow prints runtime statistics and the result.

2017/10/19 10:25:13 <- align.align  80b24fa0 ok     exec 19m27s 13.2GiB
2017/10/19 10:25:13 total n=2 time=1h18m24s
	ident           n   ncache runtime(m) cpu               mem(GiB)    disk(GiB)      tmp(GiB)
	align.align     1   0      19/19/19   123.9/123.9/123.9 7.8/7.8/7.8 12.9/12.9/12.9 0.0/0.0/0.0
	align.reference 1   0      57/57/57   7.8/7.8/7.8       4.4/4.4/4.4 8.0/8.0/8.0    0.0/0.0/0.0
	
file(sha256=sha256:becb04856590893048e698b1c4bc26192105a44866a06ad8af4f7bda0104c43b, size=14196491221)
% 

Reflow represents file values by the SHA256 digest of the file's content. In this case, that's not very useful: you want the file, not its digest. Reflow provides mechanisms to export data. In this case let's copy the resulting file to an S3 bucket.

We'll make use of the "files" system module to copy the aligned file to an external S3 bucket. Modify align.rf's Main to the following (but pick an S3 bucket you own), and then run it again. Commentary is inline for clarity.

@requires(cpu := 16, mem := 24*GiB, disk := 50*GiB)	
val Main = {
	r1 := file("s3://1000genomes/phase3/data/HG00103/sequence_read/SRR062640_1.filt.fastq.gz")
	r2 := file("s3://1000genomes/phase3/data/HG00103/sequence_read/SRR062640_2.filt.fastq.gz")
	// Instantiate the system modules "files" (system modules begin
	// with $), assigning its instance to the "files" identifier. To
	// view the documentation for this module, run "reflow doc
	// $/files".
	files := make("$/files")
	// As before.
	aligned := align(r1, r2)
	// Use the files module's Copy function to copy the aligned file to
	// the provided destination.
	files.Copy(aligned, "s3://marius-test-bucket/aligned.sam")
}

And run it again:

% reflow run align.rf
2017/10/19 10:46:01 run name: marius@localhost/e18f1312
2017/10/19 10:46:02 -> align.align  80b24fa0 xfer   exec biocontainers/bwa bwa mem -M -t 16 {{reference}..37.fa {{r1}} {{r2}} > {{out}}
2017/10/19 10:47:27 <- align.align  80b24fa0 ok     exec 0s 13.2GiB
2017/10/19 10:47:28 -> align.Main   9e252400 run  extern s3://marius-test-bucket/aligned.sam 13.2GiB
2017/10/19 10:49:42 <- align.Main   9e252400 ok   extern 2m6s 0B
2017/10/19 10:49:42 total n=2 time=3m40s
	ident       n   ncache runtime(m) cpu mem(GiB) disk(GiB) tmp(GiB)
	align.Main  1   0                                        
	align.align 1   1                                        
	
val<>

Here we see that Reflow did not need to recompute the aligned file, it is instead retrieved from cache. The reference index generation is skipped alltogether. Status lines that indicate "xfer" (instead of "run") means that Reflow is performing a cache transfer in place of running the computation. Reflow claims to have transferred a 13.2 GiB file to s3://marius-test-bucket/aligned.sam. Indeed it did:

% aws s3 ls s3://marius-test-bucket/aligned.sam
2017-10-19 10:47:37 14196491221 aligned.sam

1000align

This code was modularized and generalized in 1000align. Here, fastq, bam, and alignment utilities are split into their own parameterized modules. The toplevel module, 1000align, is instantiated from the command line. Command line invocations (reflow run) can pass module parameters through flags (strings, booleans, and integers):

% reflow run 1000align.rf -help
usage of 1000align.rf:
  -out string
    	out is the target of the output merged BAM file (required)
  -sample string
    	sample is the name of the 1000genomes phase 3 sample (required)

For example, to align the full sample from above, we can invoke 1000align.rf with the following arguments:

% reflow run 1000align.rf  -sample HG00103 -out s3://marius-test-bucket/HG00103.bam

In this case, if your account limits allow it, Reflow will launch additional EC2 instances in order to further parallelize the work to be done. (Since we're aligning multiple pairs of FASTQ files). In this run, we can see that Reflow is aligning 5 pairs in parallel across 2 instances (four can fit on the initial m4.16xlarge instance).

% reflow ps -l
marius@localhost/66986b84 align.align.sam 1:43PM 0:00 running 7.8GiB  4.7  6.5GiB   bwa ec2-52-40-140-59.us-west-2.compute.amazonaws.com:9000/21d327bb4da2b7cf/0551e1353c385dc420c00d93ff5b645c5b6dd022986d42fabb4003d9c632f383
marius@localhost/66986b84 align.align.sam 1:43PM 0:00 running 10.5GiB 58.1 4.2GiB   bwa ec2-52-40-140-59.us-west-2.compute.amazonaws.com:9000/21d327bb4da2b7cf/a39f5f6d8a8ed8b3200cc1ca6b6497dd6bc05ad501bd3f54587139e255972f21
marius@localhost/66986b84 align.align.sam 1:44PM 0:00 running 8.6GiB  50.8 925.0MiB bwa ec2-52-40-140-59.us-west-2.compute.amazonaws.com:9000/5cbba84c0ca1cb4e/5a956304a2cd3ec37db6240c82a97b4660803f2ea30f2b69139a384cd0664f68
marius@localhost/66986b84 align.align.sam 1:44PM 0:00 running 9.1GiB  3.3  0B       bwa ec2-52-40-140-59.us-west-2.compute.amazonaws.com:9000/5cbba84c0ca1cb4e/5ebd233dc5495c5c090c5177c7bfdecc1882972cf3504b532d22be1200e7e5f1
marius@localhost/66986b84 align.align.sam 1:48PM 0:00 running 6.8GiB  31.5 0B       bwa ec2-34-212-44-28.us-west-2.compute.amazonaws.com:9000/f128adf14a7a3d5a/6c1266c7c3e8fc9f2a56e370b552a163e14bf6f4ae73b7b5b067d408eb38dbcf

When it completes, an approximately 17GiB BAM file is deposited to s3:

% aws s3 ls s3://marius-test-bucket/HG00103.bam
2017-10-24 20:25:48 18752460252 HG00103.bam

A note on Reflow's cluster manager

Reflow comes with a built-in cluster manager, which is responsible for elastically increasing or decreasing required compute resources. The AWS EC2 cluster manager keeps track of instance type availability and account limits, and uses these to launch the most appropriate set of instances for a given job. When instances become idle, they will terminate themselves if they are about to cross a billing boundary (assumed to be 1h on EC2); idle instances are reused when possible.