The document discusses real-time analytics using DataStax Enterprise, which integrates Cassandra, Solr, and Hadoop for effective big data solutions. It highlights the advantages of Cassandra over traditional databases and HBase, particularly in terms of scalability, eventual consistency, and ease of use. Key features of DataStax Enterprise, including indexing, security, and schema updates, are presented along with real-world applications such as real-time stock price analysis.

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DataStax Enterprise 3.x
Realtime Analytics with Solr
Jason Rutherglen

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• Big Data Engineer at DataStax
• Co-author of ‘Programming Hive’
and ‘Introduction to Solr’ from
O’Reilly
About the Presenter

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• The company behind Cassandra
• Sells DataStax Enterprise
About DataStax

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DataStax Enterprise 3.x

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• Single stack
• Cassandra
• Solr
• Hadoop
• Consulting
• Support
DataStax Enterprise

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• ZDNet article: “The biggest cloud
app of all: Netflix”
• http://zd.net/ZHtrmW
• Built on Cassandra
• “According to Cockcroft, if something
goes wrong, Netflix can continue to
run the entire service on two out of
three zones”
Cassandra at Netflix

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• Petabytes of growing data
• Hadoop is for batch work
• What are the solutions for realtime?
What is Big Data?

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• Near realtime
• 1000 millisecond latency
What is realtime?

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• Cost of scaling to petabytes
• Physical limitations
Why not relational databases?

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• Hadoop for batch
• Solr and Cassandra for realtime
• Gives most of relational capability at
1/10 the cost, scales linearly
Relational to Big Data

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• Distributed database heavy lifting
• Simple dynamo model
• Executes replication tasks extremely
well
Why Cassandra?

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• Cassandra is easier, code is readable
• Fewer moving parts
• Multi-datacenter replication
• Enables low level IO tuning
Cassandra vs. HBase

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• HBase runs on HDFS
• HDFS is not designed for random
access IO
• Multiple hacks / products to perform
random access (MapR, HDFS Jiras)
Cassandra vs. HBase

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• Cassandra is peer to peer, there is no
single point of failure (SPOF)
• The HDFS name node is a single
point of failure
Cassandra vs. HBase

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• Most of Facebook runs on MySQL
• Memcache front ends the reads
HBase at Facebook

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• Hive with Hadoop
• A vague dialect of SQL
• Requires Java for UDFs
• Relational Joins
Batch Analytics

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• Solr
• SQL features except relational joins
• Use Hive for relational joins
• CEP (Complex Event Processing)
• Storm
Realtime Analytics

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• Storm, computes results on
streaming data
CEP (Complex Event Processing)

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• Java inverted indexing library
• Text analytics is raw computation
over linear sets of data
• High speed computation engine
Lucene

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• Terms dictionary points to list
document ids (integers)
• Tokenizes text
• Complete variety of computation on
vectors of data
Inverted Indexes

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• Search server built around Lucene
• Adds faceting, distributed search
• Missed the cloud environment
features of NoSQL systems for many
years
Solr

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• Solr Cloud is a Zookeeper based
system
• New and probably not production
ready
• Playing catch up
Solr Cloud

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• High overlap with Solr
• More mature than Solr Cloud
• Less distributed features than
Cassandra
Elastic Search

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• Columns, column families, keyspaces
• Peer to peer
• Eventual consistency
• Implements basic Google BigTable
model
Cassandra Concepts

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• Both implement a log structured
merge tree file architecture
Lucene and Cassandra

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• Data is stored in Cassandra
• Data placement controlled by
Cassandra
• Solr is a secondary index (only)
DataStax Enterprise with Solr

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• Separation of church and state, eg,
data and index
DataStax Enterprise with Solr

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• Indexing is a CPU intensive task
• Not IO bound because of multi-
threading
• When a thread is flushing, other
threads are indexing, CPU is
saturated at all times
Indexing

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• IO bound, index needs to fit in RAM,
then CPU bound
• Lucene enables multithreading
queries
• Solr does not multithread queries
Queries

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• Eventual consistency, each node has
it’s own Lucene index
• Lucene segment files are not
replicated (like Solr Cloud and
ElasticSearch)
DataStax Enterprise with Solr

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• Query requests are round robin’d
across nodes automatically
Distributed Search Architecture

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• 3.0.1 is the current release of
DataStax Enterprise
DSE 3.0.1

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• Ease of re-indexing
• Re-index the entire cluster or per-
node
• Re-indexing occurs when the Solr
schema changes
New Features in DSE 3.0.1

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• Solr Cloud requires re-indexing from
an external data source such as a
relational database
New Features in DSE 3.0.1

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• DSE re-indexes directly from
Cassandra
• No custom code is required for re-
indexing
New Features in DSE 3.0.1

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• View the heap memory usage of the
field caches
• Perform capacity planning
New Features in DSE 3.0.1

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• Multithreaded re-indexing and repair
• Adding a new Solr node is fast
New Features in DSE 3.0.1

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• Kerberos and SSL security
• Security audit logging
New Features in DSE 3.0.1

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• Near realtime: per-segment filters,
facets, multivalue facets
• Solr 4.3
DataStax Enterprise 3.1

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• vNodes
• Composite keys
DataStax Enterprise 3.1

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• Multi datacenter live Solr schema
updates and re-indexing
• CQL -> Solr queries, makes porting
SQL applications easy for SQL
developers
Future

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Demo of Wikipedia

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• Details about every trade
• Tick data generated real time and is
quantitatively query-able
• Too big to query on in real time? Not
anymore!
Real World Example: Tick Data

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• Computing the moving stock price
average in real time
• Comparing multiple moving averages
for different stock_symbols
• Requires statistical analysis, group
by companies, and faceting features
Tick Data - Moving Average

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• Read latest ticks for a given company
• Query ticks for companies in specific
verticals during large events such as
press releases
• Compute deviation of stock data over
5 years for groups of companies
Tick Data Analytics - Ad Hoc
Searches

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Real Time Stocks Demo

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General

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• Like an SQL CREATE TABLE
statement
• Defines field types
• Defines fields
Schema

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• XML based configuration options for
Solr
Solr Config

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• Commits new index segment to RAM
• Avoids ‘hard’ commit fsync
Soft Commit

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Auto Soft Commit
<!-- The default high-performance update handler --> !
<updateHandler class="solr.DirectUpdateHandler2”>!
<autoSoftCommit> !
<maxTime>1000</maxTime> <!– Near Realtime of 1 second -->!
</autoSoftCommit> !
</updateHandler>!

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• Loaded for sort and facet queries
• Uses heap space
Field Cache

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• Java based API for interacting with a
Solr server
• DSE supports SolrJ/HTTP with no
changes
SolrJ / HTTP

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• Auto data type mapping
• Copy fields
• Dynamic fields
Insert data with CQL

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• Exists however is mainly useful for
debugging
• Limited functionality, queries a single
node
CQL with Solr Query

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CQL Insert Example
INSERT INTO wikipedia (key, text) !
VALUES ('1', 'when in rome')!

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How to convert applications

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• Common to convert existing SQL
applications to Big Data
• Focus on the application functionality
SQL to Solr

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• Cassandra makes all distributed
operations easy
SQL to Solr

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• SELECT * FROM wikipedia WHERE
type = ‘pdf’
• q=type:pdf
SELECT WHERE

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• SELECT title,text FROM wikipedia
• q=*:*
• fl=title,text
SELECT columns

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• SELECT COUNT(*) FROM wikipedia
WHERE type = ‘pdf’
• q=type:pdf
• Get the num found
SELECT COUNT

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• SELECT * FROM stocks ORDER BY
price ASC
• q=*:*
• sort=price asc
SELECT ORDER BY

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• SELECT AVG(price) FROM stocks
• q=*:*
• stats=true
• stats.field=price
• The average is called ‘mean’ in the
Solr results
SELECT AVG

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• SELECT AVG(price) FROM stocks
GROUP BY symbol
• q=*:*
• stats=true
• stats.field=price
• stats.facet=symbol
SELECT AVG GROUP BY

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• SELECT * FROM wikipedia WHERE
text LIKE ‘rom%’
• q=text:rom*
SELECT WHERE LIKE

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