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author | Bryan Newbold <bnewbold@archive.org> | 2018-07-19 13:10:02 -0700 |
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committer | Bryan Newbold <bnewbold@archive.org> | 2018-07-24 11:27:45 -0700 |
commit | 8ae41461f901a8142af06610a4b9f500ce4fe47f (patch) | |
tree | 17a7c99e69da8f4c105f8e3c33d0712345579e5c /scalding/src/main | |
parent | c4db53036eac90841eb4f970b77db8c1677ef75b (diff) | |
download | sandcrawler-8ae41461f901a8142af06610a4b9f500ce4fe47f.tar.gz sandcrawler-8ae41461f901a8142af06610a4b9f500ce4fe47f.zip |
variant of CdxBackfillJob that writes to TSV
Has the same test failure ("java.lang.IndexOutOfBoundsException: Index:
1, Size: 1")
Diffstat (limited to 'scalding/src/main')
-rw-r--r-- | scalding/src/main/scala/sandcrawler/CdxBackfillJob.scala | 173 |
1 files changed, 173 insertions, 0 deletions
diff --git a/scalding/src/main/scala/sandcrawler/CdxBackfillJob.scala b/scalding/src/main/scala/sandcrawler/CdxBackfillJob.scala new file mode 100644 index 0000000..a0d3e37 --- /dev/null +++ b/scalding/src/main/scala/sandcrawler/CdxBackfillJob.scala @@ -0,0 +1,173 @@ +package sandcrawler + +import cascading.property.AppProps +import cascading.tuple.Fields +import cascading.pipe.joiner._ +import com.twitter.scalding._ +import com.twitter.scalding.typed.TDsl._ +import java.util.Properties +import cascading.tap.SinkMode +import parallelai.spyglass.base.JobBase +import parallelai.spyglass.hbase.HBaseConstants.SourceMode +import parallelai.spyglass.hbase.{HBaseSource, HBasePipeConversions} + +// Type that represents a raw parsed CDX line +case class CdxLine(surt: String, + datetime: String, + url: String, + mime: String, + http_status: String, + sha1: String, + c_size: String, + offset: String, + warc: String) + + +/** + * CDX backfill: + * 1. parse CDX (all columns) + * 2. filter CDX (pdf, HTTP 200, etc) + * 3. source HBase (key column only) + * 4. left join CDX to HBase + * 5. filter to only those with null HBase key column + * 6. convert CDX fields to HBase columns + * 7. sink results to HBase + * + * TODO: I really mixed the Scalding "field-base" and "type-based" APIs here. + * Should decide on a best practice. + */ +class CdxBackfillJob(args: Args) extends JobBase(args) with HBasePipeConversions { + + import CdxBackfillJob._ + + val hbaseSource = getHBaseSource(args("hbase-table"), args("zookeeper-hosts")) + // XXX: val hbaseSink = getHBaseSink(args("hbase-table"), args("zookeeper-hosts")) + + // Parse CDX lines from text file to typed pipe + val lines : TypedPipe[String] = TypedPipe.from(TextLine(args("cdx-input-path"))) + val cdxLines : TypedPipe[CdxLine] = lines + .filter { isCdxLine } + .map { lineToCdxLine } + .filter { CdxBackfillJob.keepCdx(_) } + + val cdxRows : TypedPipe[(String, String, String, String)] = cdxLines + .map { CdxBackfillJob.cdxLineToRow } + + val existingKeys : TypedPipe[String] = hbaseSource + .read + .toTypedPipe[String]('key) + + // filters out all the lines that have an existing SHA1 key in HBase + // the groupBy statements are to select key values to join on + val newRows : TypedPipe[(String, String, String, String)] = existingKeys + .groupBy( identity ) + .rightJoin(cdxRows.groupBy(_._1)) + .toTypedPipe + .debug + .collect { case (_, (None, row)) => row } + .debug + + // convert to tuple form and write out into HBase + /* + newRows + .toPipe('key, 'c, 'cdx, 'mime) + .toBytesWritable( new Fields("key", "c", "cdx", "mime") ) + .write(hbaseSink) + */ + newRows + .write(TypedTsv[(String, String, String, String)]("dummy-output")) + +} + +object CdxBackfillJob { + + def getHBaseSource(hbase_table: String, zookeeper_hosts: String) : HBaseSource = { + return HBaseBuilder.build( + hbase_table, + zookeeper_hosts, + List("file:size"), // not actually needed + SourceMode.SCAN_ALL) + } + + def getHBaseSink(hbase_table: String, zookeeper_hosts: String) : HBaseSource = { + return HBaseBuilder.buildSink( + hbase_table, + zookeeper_hosts, + List("f:c", "file:cdx", "file:mime"), + SinkMode.UPDATE) + } + + def normalizeMime(raw: String) : String = { + + val NORMAL_MIME = List("application/pdf", + "application/postscript", + "text/html", + "text/xml") + + val lower = raw.toLowerCase() + NORMAL_MIME.foreach(norm => + if (lower.startsWith(norm)) { + return norm + } + ) + + // Common special cases + if (lower.startsWith("application/xml")) { + return "text/xml" + } + if (lower.startsWith("application/x-pdf")) { + return "application/pdf" + } + return lower + + } + + def isCdxLine(line: String) : Boolean = { + // malformated or non-CDX11 lines + !(line.startsWith("#") || line.startsWith(" ") || line.startsWith("filedesc") || + line.split(" ").size != 11) + } + + def keepCdx(line: CdxLine) : Boolean = { + // TODO: sha1.isalnum() and c_size.isdigit() and offset.isdigit() and dt.isdigit() + if (line.http_status != "200" || line.sha1.size != 32) { + return false + } + // TODO: '-' in (line.surt, line.datetime, line.url, line.mime, line.c_size, line.offset, line.warc) + return true + } + + // Returns (key, f:c, file:cdx, file:mime), all as strings, which is close to + // how they will be inserted into HBase + def cdxLineToRow(line: CdxLine) : (String, String, String, String) = { + + val key = "sha1:" + line.sha1 + + val warcFile = line.warc.split('/')(1) + + // Read CDX-style datetime and conver to ISO 8601 with second resolution + val dtFormat = new java.text.SimpleDateFormat("yyyyMMddHHmmss") + val isoFormat = new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss'Z'") + // TODO: timezones? UTC to UTC, so I don't think so. + val dtIso = isoFormat.format(dtFormat.parse(line.datetime)) + + // warc_file = warc.split('/')[-1] + // dt_iso = datetime.strptime(dt, "%Y%m%d%H%M%S").isoformat() + // f:c = dict(u=url, d=dt_iso, f=warc_file, o=int(offset), c=1) + + // This is the "f:c" field. 'i' intentionally not set + val heritrixInfo = "" + + // file:cdx = dict(surt=surt, dt=dt, url=url, c_size=int(c_size), + // offset=int(offset), warc=warc) + val fileCdx = "" + (key, heritrixInfo, fileCdx, line.mime) + } + + def lineToCdxLine(line: String) : CdxLine = { + val raw = line.split("\\s+") + // surt, datetime, url, mime, http_status, sha1, SKIP, SKIP, c_size, offset, warc + CdxLine(raw(0), raw(1), raw(2), raw(3), raw(4), raw(5), raw(8), raw(9), raw(10)) + } + +} |