I love Scala. It enables me to write concise / dense and clear code at the same time.
Take for example the piece of code below.
Had I written it in Java, it would be much longer, while at the same time less dense, and with much less clarity of what it is doing.
val site = config.sites.find(_.name == siteName).getOrElse(
throw new IllegalArgumentException("Cannot find site " + siteName))
val group = config.groups.find(_.name == groupName).getOrElse(
throw new IllegalArgumentException("Cannot find group " + groupName))
group.repositories.foreach { repo =>
val repoDir = new File(group.path, repo.name)
println("Backing up to repository %s at %s starting..." format (repo.name, repoDir))
if (repoDir.mkdir())
println("Created directory %s" format repoDir)
repo.facets.foreach { facet =>
println("Exporting facet %s..." format facet.name)
val source = site.databases.find(_.name == facet.source).getOrElse(
throw new IllegalArgumentException(
"Facet %s references non-existing database %s" format (facet.name, facet.source)))
println("Source database is %s:%s at %s" format (source.kind, source.name, source.url))
(source.kind, facet) match {
case ("neo4j", Facet(name, sourceName, "node", typeName, "json")) =>
val outFileName = new File(name + ".json")
println("Exporting %s nodes to %s" format (typeName, outFileName))
val dbDir = new File(new URI(source.url))
println("Neo4j database directory: %s" format dbDir)
val graphDb = new EmbeddedReadOnlyGraphDatabase(dbDir.getPath)
val (rowMeta, rows) = try {
val exporter = new Neo4jNodeExporter(graphDb, typeName)
println("Fetching meta...")
val rowMeta = exporter.fetchMeta()
println("Columns are: " + rowMeta.columns.mkString(", "))
val rows = exporter.export(rowMeta)
(rowMeta, rows)
} finally {
graphDb.shutdown()
}
println("Reading rows...")
val mapper = new ObjectMapper
mapper.getSerializationConfig.set(Feature.INDENT_OUTPUT, true)
val jsonRows = rows.map { row =>
val obj = mapper.createObjectNode()
for ((value, i) <- row.values.view.zipWithIndex) {
if (value != null)
obj.put(rowMeta.columns(i), value)
}
obj
}
val jsonArray = mapper.createArrayNode().addAll(jsonRows.toList)
val jsonData = mapper.createObjectNode()
jsonData.put("data", jsonArray)
val outFile = new File(repoDir, outFileName.toString)
print("Writing %s..." format outFile)
mapper.writeValue(outFile, jsonData)
println(" [OK]")
case x: Any => println("Skipping unrecognized facet: " + x)
}
}
println("Backing up to repository %s finished." format repo.name)
}
Scala is also fantastic at storing ad-hoc object graph / trees, check a look a this :
sites = List(
Site(name = "dev", databases = List(
Database(name = "graph", kind = "neo4j", url = "file:///together/project/SatukanCinta/satukancinta-neo4j-db_dev_1.6/"))),
Site(name = "test",
databases = List(Database(name = "graph", kind = "neo4j", url = "file:///together/project/SatukanCinta/dumptest/graph")))),
groups = List(Group(name = "main", path = "/together/project/SatukanCinta/dump_main",
repositories = List(
Repository(name = "like", kind = "*", facets = List(
Facet(name = "user", source = "graph", primitive = "node", typeName = "com.satukancinta.domain.User", format = "json"),
Facet(name = "topic", source = "graph", primitive = "node", typeName = "com.satukancinta.domain.Interest", format = "json"),
Facet(name = "like", source = "graph", primitive = "relationship", typeName = "LIKE", format = "graphml"))))))) {
Beat that, Java!
Still don't believe me? How about processing a bunch of collections, and sprinkle built-in parallel capability :
val indexHits = graphDb.getAllNodes.par.filter(_.getProperty("__type__") == typeName)
log.info("Index for {} returned {} nodes", typeName, indexHits.size)
val columnNames = indexHits.par.flatMap( node =>
node.getPropertyKeys.filter( _ != "__type__" ) ).toSet
val sortedColumns = columnNames.toList.sorted
log.info("Columns for {}: {}", typeName, sortedColumns.mkString(", "))
RowMeta(columns = sortedColumns)
I really can't imagine doing that (including the concurrency) in Java. Phew.
Verbose code == easy to read ? Not always. This one is much easier on the eyes.
Tip: To learn more about Scala programming, I recommend Programming in Scala: A Comprehensive Step-by-Step Guide, 2nd Edition.
