Big data cache-surge-2012

Big DataWithout a Big Database Kate Matsudaira Decide.com @katemats

Two kinds of data “reference”, “non- nicknames “user”, “transactional” transactional” •  product/oﬀer catalogs •  user accounts •  service catalogs •  shopping cart/ordersexamples: •  static geolocation data •  user messages •  dictionaries •  … •  …created/modiﬁed users business (you)by:sensitivity to high lowstaleness:plan for growth: hard easyaccess read/write mostly readoptimization:

user datareference data

Reference Data Needs Speed credit:: http://cache.gawker.com

Performance Remindermain memory read 0.0001 ms (100 ns)network round trip 0.5 ms (500,000 ns) disk seek 10 ms (10,000,000 ns) source: http://www.cs.cornell.edu/projects/ladis2009/talks/dean-keynote-ladis2009.pdf

availability performance problemsThe Beginning problems service webapp load balancer load balancer service BIG  DATABASE webapp service data loader scalability problems

operational overheadReplication REPLICA service webapp load balancer load balancer service BIG  DATABASE webapp service data loader performance problems scalability problems

operationalLocal Caching scalability overhead problems REPLICA cache service webapp load balancer load balancer cache service BIG  DATABASE webapp cache service data loader performance long tail performance problems problems consistency problems

80% of requests query 10% of entries (head)The Long 20% of requests Tail query remaining 90% of entries (tail)Problem

operational performanceBig Cache long tail overhead problems performance problems replica service webappload balancer load balancer database BIG service CACHE webapp data loader service preload scalability consistency problems problems

Do I look like I memcached(b) need a cache? ElastiCache (AWS) Oracle Coherence

Dynamically Targeted assign keys to generic data/ the “nodes” use cases. Scales Dynamically horizontally rebalances data No assumptions Poor about loading/ performance updating data on cold starts

Big Cache Technologies•  Additional hardware•  Additional conﬁguration•  Additional monitoring } operational overhead•  Extra network hop•  Slow scanning•  Additional deserialization } performance

NoSQL to some operational overhead The Rescue? NoSQL Replica service load balancer webapp load balancer service NoSQL Database webapp service data loader some performance some scalability problems problems

Remote Store Retrieval Latency network remote store network client Lookup/write TCP request: response: 0.5 ms 0.5 ms TCP response: 0.5 ms read/parse response: 0.25 ms Total time to retrieve single value: 1.75 ms

Total time to retrieveA single value from remote store: 1.75 ms  from memory: 0.001 ms   (10 main memory reads)Sequential access of1 million random keys from remote store: 30 minutes  from memory: 1 second

The Truth About Databases

“What Im going to call as the hot data cliﬀ: As the size of your hot data set (data frequently read at sustained rates above disk I/O capacity) approaches available memory, write operation bursts that exceeds disk write I/O capacity can create a trashing death spiral where hot disk pages that MongoDB desperately needs are evicted from disk cache by the OS as it consumes more buﬀer space to hold the writes in memory.” MongoDB Source: http://www.quora.com/Is-MongoDB-a-good-replacement-for-Memcached

“Redis is an in-memory but persistent on diskdatabase, so it represents a diﬀerent trade oﬀ where very high write and read speed is achieved with the limitation of data sets that cant be larger than memory.”  Redis source: http://redis.io/topics/faq

They are fast if everything fits into memory.

Can you keep it inmemory yourself? operational relief full data service cache loader load balancer load balancer webapp full data BIG  service cache loader DATABASE webapp full data service cache loader consistency performance scales problems gain infinitely

Fixing 1.  Deployment “Cells”Consistency 2.  Sticky user sessions deployment cell full data webapp service cache loader load balancer full data webapp service cache loader full data webapp service cache loader

credit: http://www.fruitshare.ca/wp-content/uploads/2011/08/car-full-of-apples.jpeg

"Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and theseattempts at eﬃciency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small eﬃciencies, say about97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%.” Donald Knuth

How do you fit allthat data in memory?

The Answer1 2 3 4 5

“Domain Layer (or Model Layer): Responsible for representing concepts of the business, information about the business situation, and business rules. State thatDomain reﬂects the business situation is controlled and used here, even though the technical Model details of storing it are delegated to the infrastructure. This layer is the heart of business software.”Design Eric Evans, Domain-Driven Design, 20031 2 3 4 5

Domain Model Design Guidelines #3 Optimize#1 Keep it Data immutable #2 Use independent Help! I am hierarchies in the trunk! http://alloveralbany.com/images/bumper_gawking_dbgeek.jpg

intern()your immutables K1 V1 K1 V1 A A B D B C E C D K2 V2 K2 V2 E F E’ B’ D’ F C’ E’

private final Map<Class<?>, Map<Object, WeakReference<Object>>> cache = new ConcurrentHashMap<Class<?>, Map<Object, WeakReference<Object>>>(); public <T> T intern(T o) { if (o == null) return null; Class<?> c = o.getClass(); Map<Object, WeakReference<Object>> m = cache.get(c); if (m == null) cache.put(c, m = synchronizedMap(new WeakHashMap<Object, WeakReference<Object>>())); WeakReference<Object> r = m.get(o); @SuppressWarnings("unchecked") T v = (r == null) ? null : (T) r.get(); if (v == null) { v = o; m.put(v, new WeakReference<Object>(v)); } return v; }

Use Independent Hierarchies Product Summary productId = … Productid = … Offerstitle= … productId = … Offers Specifications productId = … Specifications Product Reviews Info Description productId = … Reviews Model History productId = … Rumors Description Model History productId = … Rumors productId = …

Collection Optimization1 2 3 4 5

Leverage Primitive Keys/Values collection with 10,000 elements [0 .. 9,999] size in memoryjava.util.ArrayList<Integer> 200K java.util.HashSet<Integer> 546K gnu.trove.list.array.TIntArrayList 40K gnu.trove.set.hash.TIntHashSet 102K Trove (“High Performance Collections for Java”)

Optimizesmall immutable collectionsCollections with small number of entries (up to ~20): class ImmutableMap<K, V> implements Map<K,V>, Serializable { ... } class MapN<K, V> extends ImmutableMap<K, V> { final K k1, k2, ..., kN; final V v1, v2, ..., vN; @Override public boolean containsKey(Object key) { if (eq(key, k1)) return true; if (eq(key, k2)) return true; ... return false; } ...

java.util.HashMap: Space 128 bytes + 32 bytes per entry Savings compact immutable map: 24 bytes + 8 bytes per entry

Numeric Data Optimization1 2 3 4 5

Price History Example

Problem: •  Store daily prices for 1MExample: products, 2 oﬀers per product Price •  Average price history length per product ~2 years History Total price points: (1M + 2M) * 730 = ~2 billion

Price History TreeMap<Date, Double> First 88 bytes per entry * 2 billion = ~180 GBattempt

Typical Shopping Price History price$100 0 20 60 70 90 100 120 121 days

Run Length Encodinga a a a a a b b b c c c c c c 6 a 3 b 6 a

Price History •  positive: price (adjusted to scale) Optimization •  negative: run length (precedes price) •  zero: unavailable-20 100 -40 150 -10 140 -20 100 -10 0 -20 100 90 -9 80 •  Drop pennies •  Store prices in primitive short (use scale factor to represent prices greater than Short.MAX_VALUE) Memory: 15 * 2 + 16 (array) + 24 (start date) + 4 (scale factor) = 74 bytes

Reduction compared to TreeMap<Date, Double>: 155Savings times Estimated memory for 2 billion price points: 1.2 GB << 180 GB

public class PriceHistory { Price History Model private final Date startDate; // or use org.joda.time.LocalDate private final short[] encoded; private final int scaleFactor; public PriceHistory(SortedMap<Date, Double> prices) { … } // encode public SortedMap<Date, Double> getPricesByDate() { … } // decode public Date getStartDate() { return startDate; } // Below computations implemented directly against encoded data public Date getEndDate() { … } public Double getMinPrice() { … } public int getNumChanges(double minChangeAmt, double minChangePct, boolean abs) { … } public PriceHistory trim(Date startDate, Date endDate) { … } public PriceHistory interpolate() { … }

Know Your Data

Compress text1 2 3 4 5

String Compression: byte arrays •  Use the minimum character set encodingstatic Charset UTF8 = Charset.forName("UTF-‐8"); String s = "The quick brown fox jumps over the lazy dog”; // 42 chars, 136 bytes byte[] b a= "The quick brown fox jumps over the lazy dog”.getBytes(UTF8); // 64 bytes String s1 = “Hello”; // 5 chars, 64 bytes byte[] b1 = “Hello”.getBytes(UTF8); // 24 bytes byte[] toBytes(String s) { return s == null ? null : s.getBytes(UTF8); } String toString(byte[] b) { return b == null ? null : new String(b, UTF8); }

String Compression: shared prefix•  Great for URLspublic class PrefixedString { private PrefixedString prefix; private byte[] suffix; . . . @Override public int hashCode() { … } @Override public boolean equals(Object o) { … } }

String Compression:short alphanumeric case-insensitive stringspublic abstract class AlphaNumericString {  public static AlphaNumericString make(String s) {  try { return new Numeric(Long.parseLong(s, Character.MAX_RADIX)); } catch (NumberFormatException e) { return new Alpha(s.getBytes(UTF8)); } } protected abstract String value(); @Override public String toString() {return value(); } private static class Numeric extends AlphaNumericString { long value; Numeric(long value) { this.value = value; } @Override protected String value() { return Long.toString(value, Character.MAX_RADIX); } @Override public int hashCode() { … } @Override public boolean equals(Object o) { … } } private static class Alpha extends AlphaNumericString { byte[] value; Alpha(byte[] value) {this.value = value; } @Override protected String value() { return new String(value, UTF8); } @Override public int hashCode() { … } @Override public boolean equals(Object o) { … } }}

Just convert bzip2 to byte[] ﬁrst, then compress Gzip Become the master of your strings! String Compression: Large Strings Image source:https://www.facebook.com/note.php?note_id=80105080079image

JVM Tuning1 2 3 4 5

make sure to use compressed pointers use low pause (-XX:+UseCompressedOops) GC (Concurrent Mark Sweep, G1)JVM Tuning Overprovision Adjust heap by ~30% generation sizes/ratios This s#!% is heavy! Image srouce: http://foro-cualquiera.co

Print garbage collection JVMTuning If GC pauses still prohibitive thenconsider partitioning

Cache Loading full data webapp service cache loaderload balancer full data reliable ﬁle webapp service store (S3) cache loader full data webapp service cache loader “cooked” datasets

Keep the format simple Poll for (CSV, JSON) updates Final datasets should be compressed and stored (i.e. S3) Poll frequency == data inconsistency threshold Help! I amin the trunk!

Cache LoadingTime Sensitivity

Cache Loading:low time Sensitivity Data/tax-rates /date=2012-05-01 tax-rates.2012-05-01.csv.gz /date=2012-06-01 tax-rates.2012-06-01.csv.gz /date=2012-07-01 tax-rates.2012-07-01.csv.gz

Cache Loading:/prices medium/high time /full Sensitivity /date=2012-07-01 price-obs.2012-07-01.csv.gz /date=2012-07-02 /inc /date=2012-07-01 2012-07-01T00-10-00.csv.gz 2012-07-01T00-20-00.csv.gz

meow Cache is immutable, so no locking is required CacheLoadingStrategy Swap And for datasets that Works well for need to be refreshed infrequently each update updated data sets Image src:http://static.fjcdn.com/pictures/funny_22d73a_372351.jpg

Deletions can be tricky Avoid fullsynchronization Use one container per partition YARRRRRR! http://www.lostwackys.com/wacky-packages/WackyAds/capn-crud.htm

Concurrent Locking with Trove Mappublic class LongCache<V> { private TLongObjectMap<V> map = new TLongObjectHashMap<V>(); private ReentrantReadWriteLock lock = new ReentrantReadWriteLock(); private Lock r = lock.readLock(), w = lock.writeLock(); public V get(long k) { r.lock(); try { return map.get(k); } finally { r.unlock(); } } public V update(long k, V v) { w.lock(); try { return map.put(k, v); } finally { w.unlock(); } } public V remove(long k) { w.lock(); try { return map.remove(k); } finally { w.unlock(); } }}

I am “cooking” Periodically the data sets. generate Ha! serialized data/ state Validatewith CRC or hash Keep local copies

Dependent Caches service instance cache Aload balancer service status dependencies aggregator cache B cache C health check (servlet) cache D

Deployment Cell Status deployment cell webapp status aggregator load balancer health HTTP or JMX service 1 check cell status status aggregator aggregator service 2 status aggregator

Hierarchical Status Aggregation

http://ginormasource.com/humor/wp-content/uploads/2011/10/girls-on-scale-funny-photo.jpg

Data doesn’t ﬁt Keep a smaller into the heap heap http://pryncepality.com/wp-content/uploads/2012/06/2reasons.jpg

Partitioning Decision Tree yes does my no data fit in a single VM? yes can I no partition statically? don’t use fixed use dynamicpartition partitioning partitioning difficulty

FixedPartitions http://www.nhp-spfd.com/toilet_partitions/fullsize/plastic_laminate_toilet_partitions_fs.jpg

Fixed Partition Assignment deployment cell p 1 p 2 webapp p 3 p 4 load balancer p 1 p 2 webapp p 3 p 4

Dynamic Partitioning http://pryncepality.com/wp-content/uploads/2012/06/2reasons.jpg

Dynamic Partition Assignment•  Each partition lives on at least 2 nodes: primary/secondary•  Partition (set) determined via a simple computation

Member nodes operate with“active state” from the New Eleader node Leader F Leader E A The leader is re-elected D C B

Each member node learns their targetstate from the leader E F Leader E A D C B All nodes load partitions to achieve their target state. Then will transition into an active state.

Dynamic primary secondaryPartitioning p 1 p 2 p 3 p 4 p 5 p 6 webapp load balancer load balancer p 4 p 5 p 6 p 7 p 8 p 9 webapp p 7 p 8 p 9 extra network hop is back p 1 p 2 p 3

Ad-hoc CacheQuerying Image source: http://talkfunnyjokes.com/wp-content/uploads/2011/10/20111015-134527.jpg

•  Groovy •  JRuby Ad-hoc •  MVEL   Querying (http://mvel.codehaus.org)Languages •  JoSQL   (Java Object SQL, http://josql.sourceforge.net)

Break up the query: •  Extractor expressions (like SQL “SELECT” clause)Organizing •  Filter (evaluates to boolean, like SQL “WHERE” clause) Queries •  Sorter (like SQL “ORDER BY” clause) •  Limit (like SQL “LIMIT” clause)

prune partitions partition 1 partition 2 partition N … filter filter filter sort sort sort limit limit limit intermediate intermediate intermediate results results results sort Parallel limit (fan-out) final resultsQuery Execution extract

Parallel (fan-out) Query: Multi-level Reductionp 1 p 2 p 3 p M p M+1 p N r 1 r 2 r 3 r 4

Optimizing for Network Topology Using Multi-level Reductionslower/lower capacity linksfaster/higher capacity links p 1 p 2 p 7 p 8 p 13 p 14 p 3 p 4 p 9 p 10 p 15 p 16 p 5 p 6 p 11 p 12 p 17 p 18 location 1 location 2 location 3

Example (from JoSQL site): SELECT * FROM java.io.File WHERE name $LIKE "%.html" JoSQL AND toDate (lastModified) BETWEEN toDate (01-‐12-‐2004) AND toDate (31-‐12-‐2004)

find average number of significant price changes and average pricehistory by product category for a specific seller and time period (firsthalf of 2012)select @priceChangeRate, product, @avgPriceHistory JoSQL Examplefrom products where offers.lowestPrice >= 100 and offer(offers, 12).priceHistory.startDate <= date(2012-01-01) and offer(offers, 12).priceHistory.endDate >= date(2012-06-01) and decideRank.rank < 200group by category(product.primaryCategoryId).namegroup by order 1execute on group_by_results sum(getNumberOfDailyPriceChanges( trimPriceHistory(offer(offers, 12).priceHistory, 2012-01-01, 2012-06-01), offer(offers, 12).priceHistory.startDate, 15, 5)) / count(1) as priceChangeRate, avgPriceHistory(trimPriceHistory(offer(offers, 12).priceHistory, 2012-01-01, 2012-06-01)) as avgPriceHistory

Total time to execute where clause on all objects: 6010.0 ms where clause average over 3,393,731 objects: 0.0011 ms Total time to execute 2 expressions on GROUP_BY_RESULTS objects: Group operation 3.0 ms took: 1.0 ms Group column collection and sort: 46.0 ms

Monitoring cache readiness •  rolled up status aggregate metrics •  staleness •  total counts •  counts by partition •  counts by attribute •  average cardinality of one-to-many relationships http://dailyjokes.in/wp-content/uploads/2012/06/server.jpg

Monitoring cache readiness •  rolled up status aggregate metrics •  staleness •  total counts •  counts by partition •  counts by attribute •  average cardinality of one- to-many relationships

The End http://www.decide.com http://katemats.com And much of the credit for this talk goes to Decide’s Chief Architect, Leon Stein for developing thetechnology and creating the content. Thank you, Leon.

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Big data cache-surge-2012

by Kate Matsudaira on Sep 27, 2012

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Big data cache-surge-2012 Presentation Transcript