FiloDB

Distributed. Columnar. Versioned.

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FiloDB is a new open-source database based on Apache Cassandra and Spark SQL. FiloDB brings breakthrough performance levels for analytical queries by using a columnar storage layout with different space-saving techniques like dictionary compression. At the same time, row-level, column-level operations and built in versioning gives FiloDB far more flexibility than can be achieved using file-based technologies like Parquet alone.

FiloDB aim's to bring one to two orders of magnitude speedups over OLAP performance of Cassandra 2.x CQL tables + Spark. For the POC performance comparison, please see cassandra-gdelt repo.
Enable easy exactly-once ingestion from Kafka for streaming geospatial applications.
Incrementally computed columns and geospatial annotations
MPP-like automatic caching of projections from popular queries for fast results

FiloDB is a great fit for bulk analytical workloads, or streaming / append-only event data. It is not optimized for heavily transactional, update-oriented workflows.

Overview presentation -- see the docs folder for design docs.

To compile the .mermaid source files to .png's, install the Mermaid CLI.

Current Status

Definitely alpha or pre-alpha. What is here is more intended to show what is possible with columnar storage on Cassandra combined with Spark, and gather feedback.

Append-only
CSV ingest only, although adding additional ingestion types (like Kafa) is not hard - see CsvSourceActor.
Keyed by partition and row number only
Only int, double, long, and string types
Localhost only - no locality in Spark input source

Also, the design and architecture are heavily in flux. Currently this is designed as a layer on top of Cassandra, but may (probably will) evolve towards something that can integrate with existing C* tables.

You can help!

Send me your use cases for OLAP on Cassandra and Spark
- Especially IoT and Geospatial
Email if you want to contribute

Your feedback will help decide the next batch of features, such as: - which data types to add support for - which input sources to add (Kafka? Avro on Kafka? Save to FiloDB from Spark DataFrames? from existing C* tables?)

Building and Testing

Run the tests with sbt test, or for continuous development, sbt ~test. Noisy cassandra logs can be seen in filodb-test.log.

Using the CLI

First, build the CLI using sbt cli/assembly. This will create an executable in cli/target/scala-2.10/filo-cli-*. In the examples below, the "filo-cli" command is an alias to this executable.

Create a dataset with all the columns from the GDELT public dataset:

filo-cli --command create --dataset gdelt --columns GLOBALEVENTID:int,SQLDATE:string,MonthYear:int,Year:int,FractionDate:double,Actor1Code:string,Actor1Name:string,Actor1CountryCode:string,Actor1KnownGroupCode:string,Actor1EthnicCode:string,Actor1Religion1Code:string,Actor1Religion2Code:string,Actor1Type1Code:string,Actor1Type2Code:string,Actor1Type3Code:string,Actor2Code:string,Actor2Name:string,Actor2CountryCode:string,Actor2KnownGroupCode:string,Actor2EthnicCode:string,Actor2Religion1Code:string,Actor2Religion2Code:string,Actor2Type1Code:string,Actor2Type2Code:string,Actor2Type3Code:string,IsRootEvent:int,EventCode:string,EventBaseCode:string,EventRootCode:string,QuadClass:int,GoldsteinScale:double,NumMentions:int,NumSources:int,NumArticles:int,AvgTone:double,Actor1Geo_Type:int,Actor1Geo_FullName:string,Actor1Geo_CountryCode:string,Actor1Geo_ADM1Code:string,Actor1Geo_Lat:double,Actor1Geo_Long:double,Actor1Geo_FeatureID:int,Actor2Geo_Type:int,Actor2Geo_FullName:string,Actor2Geo_CountryCode:string,Actor2Geo_ADM1Code:string,Actor2Geo_Lat:double,Actor2Geo_Long:double,Actor2Geo_FeatureID:int,ActionGeo_Type:int,ActionGeo_FullName:string,ActionGeo_CountryCode:string,ActionGeo_ADM1Code:string,ActionGeo_Lat:double,ActionGeo_Long:double,ActionGeo_FeatureID:int,DATEADDED:string,Actor1Geo_FullLocation:string,Actor2Geo_FullLocation:string,ActionGeo_FullLocation:string

You could also add columns later with the same syntax.

Create a partition:

filo-cli --command create --dataset gdelt --partition first

Verify the dataset metadata:

filo-cli --command list --dataset gdelt

Import a CSV file:

filo-cli --command importcsv --dataset gdelt --partition first --filename GDELT_1979-1984.csv

Query/export some columns:

filo-cli --dataset gdelt --partition first --select MonthYear,Actor2Code

Using the Spark SQL query engine

Build the spark input source module with sbt spark/assembly. Then, CD into a Spark 1.3.1 distribution (1.3.0 and onwards should work), and start spark-shell with something like:

bin/spark-shell --jars ../FiloDB/spark/target/scala-2.10/filodb-spark-assembly-0.1-SNAPSHOT.jar

Create a config, then create a dataframe on the above dataset:

scala> val config = com.typesafe.config.ConfigFactory.parseString("max-outstanding-futures = 16")
config: com.typesafe.config.Config = Config(SimpleConfigObject({"max-outstanding-futures":16}))

scala> import filodb.spark._
import filodb.spark._

scala> val df = sqlContext.filoDataset(config, "gdelt")
15/06/04 15:21:41 INFO DCAwareRoundRobinPolicy: Using data-center name 'datacenter1' for DCAwareRoundRobinPolicy (if this is incorrect, please provide the correct datacenter name with DCAwareRoundRobinPolicy constructor)
15/06/04 15:21:41 INFO Cluster: New Cassandra host localhost/127.0.0.1:9042 added
15/06/04 15:21:41 INFO FiloRelation: Read schema for dataset gdelt = Map(ActionGeo_CountryCode -> Column(ActionGeo_CountryCode,gdelt,0,StringColumn,FiloSerializer,false,false), Actor1Geo_FullName -> Column(Actor1Geo_FullName,gdelt,0,StringColumn,FiloSerializer,false,false), Actor2Name -> Column(Actor2Name,gdelt,0,StringColumn,FiloSerializer,false,false), ActionGeo_ADM1Code -> Column(ActionGeo_ADM1Code,gdelt,0,StringColumn,FiloSerializer,false,false), Actor2CountryCode -> Column(Actor2CountryCode,gdelt,0,StringColumn,FiloSerializer,fals...

You could also verify the schema via df.printSchema.

Now do some queries, using the DataFrame DSL:

scala> df.select(count(df("MonthYear"))).show()
...<skipping lots of logging>...
COUNT(MonthYear)
4037998

or SQL, to find the top 15 events with the highest tone:

scala> df.registerTempTable("gdelt")

scala> sqlContext.sql("SELECT Actor1Name, Actor2Name, AvgTone FROM gdelt ORDER BY AvgTone DESC LIMIT 15").collect()
res13: Array[org.apache.spark.sql.Row] = Array([208077.29634561483])

Now, how about something uniquely Spark .. feed SQL query results to MLLib to compute a correlation:

scala> import org.apache.spark.mllib.stat.Statistics

scala> val numMentions = df.select("NumMentions").map(row => row.getInt(0).toDouble)
numMentions: org.apache.spark.rdd.RDD[Double] = MapPartitionsRDD[100] at map at DataFrame.scala:848

scala> val numArticles = df.select("NumArticles").map(row => row.getInt(0).toDouble)
numArticles: org.apache.spark.rdd.RDD[Double] = MapPartitionsRDD[104] at map at DataFrame.scala:848

scala> val correlation = Statistics.corr(numMentions, numArticles, "pearson")