Download - SSTable Reader Cassandra Day Denver 2014
Cassandra Day Denver 2014
Reading Cassandra SSTables Directly for Offline Data Analysis
Ben Vanberg@jackbyrd
FullContact
Cassandra Day Denver 2014
A Journey
● Solving a problem for a specific use case
● Implementation
● Example Code
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Person API
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Goal: Analytics on Cassandra Data
● How many queries per search type?
● How many profiles have social data and what type? (facebook, twitter, etc)
● Total social profiles of each type?
● Connections (email > twitter, twitter > email, etc)
● Response codes to staleness for cache tuning.
● Whatever!
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Key Factors
● Netflix Priam for Backups (Snapshots, Compressed)
● Size-Tiered Compaction (SSTables 200 GB+)
● Compression enabled (SnappyCompressor)
● AWS
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Where we started
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Limiting Factors
● 3-10 days total processing time
● $2700+ in AWS resources
● Ad-Hoc analytics (not really!)
● Engineering time!
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Moving Forward
● Querying Cassandra directly didn’t scale for MapReduce.
● Cassandra SSTables. Could we consume them directly?
● SSTables would need to be directly available (HDFS).
● SSTables would need to be available as MapReduce input.
● Did something already exist to do this?
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Netflix Aegisthus
● We already use Netflix Priam for Cassandra backups
● Aegisthus works great for the Netflix use case: (C* 1.0, No compression)
● At the time there was an experimental C* 1.2 branch.
● Aegisthus splits only when compression is not enabled.
● Single thread processing 200 GB+ SSTables.
● https://github.com/Netflix/aegisthus
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KassandraMRHelper
● Support for C* 1.2!
● We got the job done with KassandraMRHelper
● Copies SSTables to local file system in order to leverage existing C* I/O
libraries.
● InputFormat not splittable.
● Single thread processing 200 GB+ SSTables.
● 60+ hours to process
● https://github.com/Knewton/KassandraMRHelper
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Existing Solutions
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Implementing a Splittable InputFormat
● We needed splittable SSTables to make this work.
● With compression enabled this is more difficult.
● Cassandra I/O code makes the compression seamless but doesn’t support
HDFS.
● Need a way to define the splits.
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Our Approach
● Leverage the SSTable metadata.
● Adapt Cassandra I/O libraries to HDFS.
● Leverage the SSTable Index to define splits. IndexIndex!
● Implement an InputFormat which leverages the IndexIndex to define splits.
● Similar to Hadoop LZO implementation.
Cassandra Day Denver 2014
Cassandra SSTables
Data file: This file contains the actual SSTable data. A binary format of
key/value row data. http://en.wikipedia.org/wiki/BigTable
Index file: This file contains an index into the data file for each row key.
CompressionInfo file: This file contains an index into the data file for each
compressed block. This file is available when compression has been enabled
for a Cassandra column family.
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Cassandra I/O for HDFS
● Cassandra’s I/O allows for random access of the SSTable.
● Porting this code to HDFS allowed us to read the SSTable in the same
fashion directly within MapReduce.
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The IndexIndex
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Original Solution
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Final Solution
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Results
Reading via live queries to Cassandra 3-10 days $2700+
Unsplittable SSTable input format 60 hours $350+
Splittable SSTable input format 10 hours $165+
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Example
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Mapper
AbstractType keyType =
CompositeType.getInstance(Lists.<AbstractType<?>>newArrayList(UTF8Type.instance, UTF8Type.
instance));
protected void map(ByteBuffer key, SSTableIdentityIterator value, Context context)
throws IOException, InterruptedException {
final ByteBuffer newBuffer = key.slice();
final Text mapKey = new Text(keyType.getString(newBuffer));
Text mapValue = jsonColumnParser.getJson(value, context);
if (mapValue == null) {
return;
}
context.write(mapKey, mapValue);
}
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Reducer
protected void reduce(Text key, Iterable<Text> values, Context context)
throws IOException, InterruptedException {
// Make things super simple and output the first value only.
// In reality we'd want to figure out which was the
// most correct value of the ones we have based on our C* cluster
configuration.
context.write(key, new Text(values.iterator().next().toString()));
}
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Job Configuration
job.setMapperClass(SimpleExampleMapper.class);
job.setReducerClass(SimpleExampleReducer.class);
...
job.setInputFormatClass(SSTableRowInputFormat.class);
...
SSTableInputFormat.addInputPaths(job, inputPaths);
...
FileOutputFormat.setOutputPath(job, new Path(outputPath));
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Running the indexer
hadoop jar hadoop-sstable-0.1.2.jar com.fullcontact.sstable.
index.SSTableIndexIndexer [SSTABLE_ROOT]
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Running the job
hadoop jar hadoop-sstable-0.1.2.jar com.fullcontact.sstable.example.SimpleExample
\
-D hadoop.sstable.cql="CREATE TABLE ..." \
-D mapred.map.tasks.speculative.execution=false \
-D mapred.job.reuse.jvm.num.tasks=1 \
-D io.sort.mb=1000 \
-D io.sort.factor=100 \
-D mapred.reduce.tasks=512 \
-D hadoop.sstable.split.mb=1024 \
-D mapred.child.java.opts="-Xmx2G -XX:MaxPermSize=256m" [SSTABLE_ROOT]
[OUTPUT_PATH]
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Example Summary
1. Write SSTable reader MapReduce jobs
2. Run the SSTable Indexer
3. Run SSTable reader MapReduce jobs
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Goal Accomplished
● 96% decrease in processing times!
● 94% decrease in resource costs!
● Reduced Engineering time!
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Open Source Project
Open Source @ https://github.com/fullcontact/hadoop-sstable
Roadmap (Q4 2014):
● Cassandra 2.1 support
● Scalding support
● Hadoop 2