if the data is in SRA, there is sra-stat utility that returns reads,bases and quality distribution. these are stored in the SRA file. use --quick to get the stored stats or --statistics to calculate additional values broken down per readgroup/barcode. sra-stat --quick SRR077487

The following is more than twice as fast; however, wc counts newline characters as well. We thus need to subtract the line count from the base count (using Bash):

fix_base_count() {
    local counts=($(cat))
    echo "${counts[0]} $((counts[1] - counts[0]))"
}

gunzip -c "$file" \
| awk 'NR % 4 == 2' \
| wc -cl \
| fix_base_count

All the caveats from Simon’s comment apply: this assumes the “simple” FASTQ format, where each record consists of exactly four lines. I think this is true for all files produced by Illumina sequencers and downstream tools.

I get fairly quick results with my fastx-length.pl script, with the added bonus of being able to handle multi-line FASTQ files and displaying additional read-length QC statistics:

time zcat albacored_all.fastq.gz | /bioinf/scripts/fastx-length.pl > /dev/null
Total sequences: 301135
Total length: 283.902419 Mb
Longest sequence: 5.601 kb
Shortest sequence: 6 b
Mean Length: 942 b
Median Length: 999 b
N50: 111835 sequences; L50: 1.103 kb
N90: 245243 sequences; L90: 608 b

real    0m8,802s
user    0m16,584s
sys 0m0,260s

Versus the script you have provided:

zcat albacored_all.fastq.gz | awk 'NR%4==2{c++; l+=length($0)}
          END{
                print "Number of reads: "c; 
                print "Number of bases in reads: "l
              }'
Number of reads: 301135
Number of bases in reads: 283902419

real    0m8,382s
user    0m10,216s
sys 0m0,332s

Cat to /dev/null for comparison:

time zcat albacored_all.fastq.gz > /dev/null

real    0m7,877s
user    0m7,856s
sys 0m0,020s

I suspect that something using bioawk might be a bit faster (and similarly FASTQ-compliant).

I hava implemented seqtk_counts using kseq.h from klib

Just a few line of Codes:

#include <stdio.h>
#include <zlib.h>
#include "kseq.h"

KSEQ_INIT(gzFile, gzread)

int main(int argc, char *argv[]){

gzFile fp;
kseq_t *seq;

int l = 0;

int64_t total = 0;
int64_t lines = 0;

if (argc == 1) {
    fprintf(stderr, "Usage: %s <fastq> <sample>\n", argv[0]);

    return 1;
}

fp = strcmp(argv[1], "-")? gzopen(argv[1], "r") : gzdopen(fileno(stdin), "r");
seq = kseq_init(fp);

while ((l = kseq_read(seq)) >= 0){
    total += seq->seq.l;
    lines += 1;
}

printf("%s\t%lld\t%lld\n", argv[2] ,(long long)lines, (long long)total);
kseq_destroy(seq);
gzclose(fp);
return 0;

}

Compile it:

gcc  -O2  seqtk_counts.c  -o  seqtk_counts  -Iklib  -lz

Usage:

seqtk_counts foo.fasq.gz foo 
or
cat foo.fasq.gz | seqtk_counts  - foo

It's difficult to get this to go massively quicker I think - as with this question working with large gzipped FASTQ files is mostly IO-bound. We could instead focus on making sure we are getting the right answer.

People deride them too often, but this is where a well-written parser is worth it's weight in gold. Heng Li gives us this FASTQ Parser in C.

I downloaded the example tarball and modified the example code (excuse my C...):

#include <zlib.h>
#include <stdio.h>
#include "kseq.h"
KSEQ_INIT(gzFile, gzread)

int main(int argc, char *argv[])
{
    gzFile fp;
    kseq_t *seq;
    int l;
    if (argc == 1) {
        fprintf(stderr, "Usage: %s <in.seq>\n", argv[0]);
        return 1;
    }
    fp = gzopen(argv[1], "r");
    seq = kseq_init(fp);
    int seqcount = 0;
    long seqlen = 0;
    while ((l = kseq_read(seq)) >= 0) {
        seqcount = seqcount + 1;
        seqlen = seqlen + (long)strlen(seq->seq.s);
    }
    kseq_destroy(seq);
    gzclose(fp);
    printf("Number of sequences: %d\n", seqcount);
    printf("Number of bases in sequences: %ld\n", seqlen);
    return 0;
}

Then make and kseq_test foo.fastq.gz.

For my example file (~35m reads of ~75bp) this took:

real    0m49.670s
user    0m49.364s
sys     0m0.304s

Compared with your example:

real    0m43.616s
user    1m35.060s
sys     0m5.240s

Konrad's solution (in my hands):

real    0m39.682s
user    1m11.900s
sys     0m5.112s

(By the way, just zcat-ing the data file to /dev/null):

real    0m38.736s
user    0m38.356s
sys     0m0.308s

So, I get pretty close in speed, but am likely to be more standards compliant. Also this solution gives you more flexibility with what you can do with the data.

And my horrible C can almost certainly be optimised.

Same test, with kseq.h from Github, as suggested in the comments:

My machine is under different load this morning, so I've retested. Wall clock times:

OP: 0m44.813s

Konrad: 0m40.061s

zcat > /dev/null: 0m34.508s

kseq.h (Github): 0m32.909s

So most recent version of kseq.h is faster than simply zcat-ing the file (consistently in my tests...).

Use assembly-stats!

$ assembly-stats barcode13_filtered.fastq 
stats for barcode13_filtered.fastq
sum = 2080834976, n = 656192, ave = 3171.08, largest = 15321
N50 = 3598, n = 225863
N60 = 3263, n = 286569
N70 = 2920, n = 353904
N80 = 2548, n = 429982
N90 = 2081, n = 519671
N100 = 500, n = 656192
N_count = 0
Gaps = 0

real    0m7.418s
user    0m5.976s
sys     0m1.275s

Works on any fasta/fastq files, even those that aren't assemblies. And it's pretty fast!

Download from here: https://anaconda.org/bioconda/assembly-stats

pigz | awk | wc is the fastest method

First off for benchmarks with FASTQ it's best to use a specific real-world example with a known answer. I've chosen this file:

ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/data/HG01815/sequence_read/ERR047740_1.filt.fastq.gz

as my test file, the correct answers being:

Number of reads: 67051220
Number of bases in reads: 6034609800

Next we want to find the fastest way possible to count these, all timings are the average wall-clock time (real) of 10 runs collected with the bash time on an otherwise unloaded system:

zgrep

zgrep . ERR047740_1.filt.fastq.gz |
     awk 'NR%4==2{c++; l+=length($0)}
          END{
                print "Number of reads: "c; 
                print "Number of bases in reads: "l
              }'

This is the slowest method with an average run-time of 125.35 seconds

gzip awk

Using gzip we gain about another 10 seconds:

gzip -dc ERR047740_1.filt.fastq.gz |
     awk 'NR%4==2{c++; l+=length($0)}
          END{
                print "Number of reads: "c; 
                print "Number of bases in reads: "l
              }'

Average run-time is 116.69 seconds

Konrad's gzip awk wc variant

fix_base_count() {
    local counts=($(cat))
    echo "${counts[0]} $((${counts[1]} - ${counts[0]}))"
}

gzip -dc ERR047740_1.filt.fastq.gz \
    | awk 'NR % 4 == 2' \
    | wc -cl \
    | fix_base_count

This runs slower on this test file than the gzip awk variant of the solution, average run-time is 122.28 seconds.

kseq_test using latest kseq.h from klib

Code compiled with: gcc -O2 -o kseq_test kseq_test.c -lz where kseq_test.c is Simon's adaptation of Heng Li's FASTQ parser.

kseq_test ERR047740_1.filt.fastq.gz

Average run-time is 99.14 seconds, which is better than the gzip core utilities based solution so far, but we can do better!

piz awk

Using Mark Adler's pigz as a drop-in replacement for gzip, note that pigz gives us a speed gain as on top of gzip as in addition to the main deflate thread it uses another 3 threads for reading, writing and checksum calculations, see the man page for details.

pigz -dc ERR047740_1.filt.fastq.gz |
     awk 'NR%4==2{c++; l+=length($0)}
          END{
                print "Number of reads: "c; 
                print "Number of bases in reads: "l
              }'

Average run-time is now 93.86 seconds, this is ~5 seconds faster than the kseq based C code but we can further improve the benchmark.

pigz awk wc

Next we use pigz as a drop in replacment for Konrad's wc variant of the awk based solution.

fix_base_count() {
    local counts=($(cat))
    echo "${counts[0]} $((${counts[1]} - ${counts[0]}))"
}

gzip -dc ERR047740_1.filt.fastq.gz \
    | awk 'NR % 4 == 2' \
    | wc -cl \
    | fix_base_count

Average run-time is now down to 83.03 seconds, this is ~16 seconds faster than the kseq based solution and ~42 seconds faster than the OPs zgrep based solution.

Next as a baseline lets see just how much of this run-time is due to decompression of the input fastq.gz file.

gzip alone

gzip -dc ERR047740_1.filt.fastq.gz > /dev/null

Average run-time: 105.95 seconds, so the gzip based solutions (which also includes zcat and zgrep as these are provided by gzip) are never going to be faster than kseq_test.

pigz alone

pigz -dc ERR047740_1.filt.fastq.gz > /dev/null

Average run-time: 77.66 seconds, so quite clearly the additional three threads for read, write and checksum calculation offer a useful advantage. What's more this speed-up is greater when leveraging the awk | wc based solution, it's not clear why, but I expect this is due to the extra write thread.

Interestingly average CPU usage across all threads is quite revealing for the various answers, I've collated these stats using GNU time /usr/bin/time --verbose

zgrep based solution 133% - must be more than one thread somehow

gzip | awk based solution 99% - all gzip based solutions run single-threaded at 99% CPU usage

pigz | awk 147%

gzip | awk | wc 99% as with gzip

pgiz | awk | wc 155%

kseq_test 99%

gzip > dev/null 99%

pigz > dev/null 155%

Whilst the main deflate thread in pigz will run at 100% CPU load the extra 3 don't quite fully occupy additional cores to 100% (as is evidenced by average CPU usage of ~150%) they do however clearly result in reduced run-time.

I'm using Ubuntu 16.04.2 LTS**, my gzip, zcat, zgrep versions are all gzip 1.6 and pigz is version 2.3.1. gcc is version 5.4.0

** I think my patch level is actually 16.04.4 but I've not rebooted for 170 days :p

Using pyGATB

(I use the same file as in https://bioinformatics.stackexchange.com/a/400/292, same workstation as in https://bioinformatics.stackexchange.com/a/380/292)

$ time python3 -c "from gatb import Bank; seq_lens = [len(seq) for seq in Bank('SRR077487_2.filt.fastq.gz')]; print('Number of reads: %d' % len(seq_lens), 'Number of bases in reads: %d' % sum(seq_lens), sep='\n')"
Number of reads: 23861612
Number of bases in reads: 2386161200

real    0m41.122s
user    0m40.788s
sys     0m0.312s

It is quite faster than bioawk:

$ time bioawk -c fastx '{nb_seq+=1; nb_char+=length($seq)} END {print "Number of reads: "nb_seq"\nNumber of bases in reads: "nb_char}' SRR077487_2.filt.fastq.gz
Number of reads: 23861612
Number of bases in reads: 2386161200

real    1m3.182s
user    1m2.916s
sys     0m0.268s

But not so much than the OP example:

$ time zgrep . SRR077487_2.filt.fastq.gz | awk 'NR%4==2{c++; l+=length($0)} END{print "Number of reads: "c; print "Number of bases in reads: "l}'
Number of reads: 23861612
Number of bases in reads: 2386161200

real    0m47.127s
user    1m36.292s
sys     0m6.796s

Or than the wc based solution:

$ fix_base_count() {
>     local counts=($(cat))
>     echo "${counts[0]} $((${counts[1]} - ${counts[0]}))"
> }
$ time gunzip -c SRR077487_2.filt.fastq.gz | awk 'NR % 4 == 2' | wc -cl | fix_base_count
23861612 2386161200

real    0m44.915s
user    1m12.000s
sys     0m6.972s

I didn't compare with C-based solutions.

The zcat to /dev/null reference is the following:

$ time zcat SRR077487_2.filt.fastq.gz > /dev/null

real    0m39.745s
user    0m39.464s
sys     0m0.252s

I'm still impressed by pyGATB speed