Cloud service vs Software as a service

Everyday we use some awesome cloud services or applications like Gmail, Whatsapp, Waze etc.

If I write a web application and put it on a server that I rent from a hosting service at $3.99 a month, is it a cloud service or is it "software as a service" ?. Or is it just a plain vanilla web application ?

Even if I am write a modern application, and it is hosted on AWS or google cloud, does that automatically make it a "cloud" application ?

Today, no software company says, we are "software as a service". Everyone says they have a cloud service.

In this blog, I describe the characteristics that makes an application a real "cloud" application.

An example of a real cloud application is Gmail. As long as I have a connection to the internet, I am always able to access my mail. I can access it from any browser, any mail client, any phone, any device. I can access my email from any place in the world. A billion other people trying to access their emails at the same time does not affect me. I can still do my email stuff. If I try to get an email that I got 10 years ago, even though I am communicating with some server on the west coast, that may not have that data, gmail will get the data from a server that has stored that email. If that server is down, gmail will get it from another server in the same data center that has a replica of the data. If the entire data center is down, gmail will get it from another data center in the same region. If the entire region is down, gmail might get my email from a server in a data center in completely different region say Europe.

The characteristics of a real cloud service are :

(1)  Location independence

A user of a cloud service must be able to use the service from any location  without any degradation in service.

If the service has just one server in mountain view, then when I travel to China, accessing it is going to be horribly slow.

The location independence comes from geographically distributing servers and replicating data to where it is served.

(2) Scale horizontally

As the service becomes popular and the number of users go up, the number of requests go up, the data size goes up, there should be no degradation in service. It should scale by adding more servers.
Load balancers will distribute requests to a clusters of servers.

(3) Highly available

Service should be available 24*7. You have data replication and redundancy built in. A failure of a server and even a data center should not lead to stoppage of service

(4) Device independence

You should be able to access the service from any device that can access the internet - browser, mobile device, IOT etc.

(5) Self healing

The service infrastructure should monitor itself , detect failures early , so that down times are minimal

(6) Commodity hardware and (open source software)

Given the scale of a real cloud service, even for the large companies, it is affordable only using commodity hardware and software.

(7) Micro services

The software is generally built as micro services that communicate using simple protocols like REST. Monolithic applications are harder to maintain and fix.

Gmail, amazon shopping website, Waze, Whatspp etc are examples of real cloud applications. Under the hood they are powered by real cloud scale infrastructures.

The good news for the rest of us building cloud applications is that we do not have to build every thing from scratch. There are 2 broad options

Option 1 : Rent physical cloud but build software and data infrastructure

First there is the physical cloud : You needs machines either physical or virtual on the internet, distributed and across many regions. This part can be rented from Cloud vendors like Amazon, Google, Microsoft and others. You will not want to build a physical cloud unless you are close to being another Google or Amazon.

Then there is the data and software part. These are the micro service you build, the distributed databases and message brokers you use. You do the management of data , the replication, the software scaling. There are many open source frameworks , databases , caches, message brokers to help.

A good approach is to build and test the software locally with characteristics listed above and then deploy to the physical cloud for production.

The advantage of this approach is the your service will work on a physical cloud from any vendor. It works even if you decide to run it off the internet or "in premise"/intranet.

Option 2: Rent platform as a service

If you prefer not to deal with infrastructure, cloud vendors have combined the physical cloud and software into "platform as a service". Google App engine or AWS lamda , RDS are examples of this.
Here the cloud vendor manages both the physical cloud and software infrastructure and you will  write just the application code. The downside of this approach is vendor lock in. This is appropriate if you do not have the relevant expertise for option 1.

Summary

In summary a "real" cloud application is one that scales horizontally and is highly available with the same quality of service  irrespective of where the user is, what device he uses or how many users are using the service at a time. Simply writing a monolithic application and putting it on amazon ec2 or google compute is not a cloud service.

However if you design and build your application with the characteristics listed above, your application is "cloud" ready. You can deploy it to a physical cloud anytime.

Cache consistency issues in distributed applications

Your typical enterprise web application is

Going to the database for every read or write is expensive. Developers try to improve read performance by storing values in a cache like memcached or redis.

Cache is in memory storage. Performance is greatly improved by reading from memory than going to secondary storage like disk where database or files.

On reads, the application first checks cache. If the value is found in cache, it read from there. On a cache miss, the app will read from database and then update the cache so the subsequent reads do not go to the database.

On writes,the application needs to write to the database and update the cache as well, so the subsequent reads get the updated value.

The approach of using a cache to improve read performance works very well when your reads greatly outnumber writes. That is say most requests are reading ( say 80%) and few requests update the data.

Frequent writes or updates to data complicate matters. Any writes to the database need to be reflected in the cache.

1.0 Common mistakes with caches:

These problems are mostly caused by multiple clients threads (improperly) updating the cache.

1.1. Race condition between reader / writer threads

Thread 1 wants to read a value.
It goes to cache and does not find it.
It reads the value from DB

Thread 2 updates the value in DB and updates the cache

Thread 1 sets the outdated value in cache.
Until there is another update to the same value, every one is reading the outdated value.

1.2 Race condition between writer threads

Minor variation of  1.1

 At time t1, thread1 updates database value x to x1

At time t2, thread2 update database value to x2.
thread2 updates cache value to x2.

thread1 overwrites x2 to x1.

Subsequent readers are reading an incorrect value x1.

Soln : locking x in cache, update database, update cache , release lock on x
downside : locking in 2 places cache and db deadlocks

1.3  Cache not cleaned up on database rollback

This happens when cache is updated prior to database transaction commit.

thread 1 update value in db
before the transaction commits, it updates the cache
transaction rolls back
cache has outdated value

1.4 Reading before commit

This is a rare situation that could happen when cache is updated post database transaction commit.

Thread 1 is in the process of updating a value x.
x is uncommitted.
cache is not updated.

Other parts of code in Thread read the value from cache for other purposes. They reading an out dated value.

Soln: A thread that needs to reuse values it changed should store values locally and use from local until the value is committed to both database and cache.

2.0 Strategies for elimination cache race conditions :

2.1 Locking the value in cache

The strategy is

-- lock the value to be updated in cache
-- update in database
-- update in cache
-- unlock the cache lock

While this can work, the disadvantage of this approach is

-- locking twice. Database transaction does some locking. Now we have additional locking in cache. Negative for performance
-- Improper locking can lead to deadlocks

2.2 Checking timestamps and/or previous values

In the cache , in addition to value, store the update timestamp from db.
Before updating the cache, check the timestamp and only update if you have a latter timestamp.

If you do not want the overhead of storing timestamp in cache, another approach could

-- 1 previous value =   read the cache value before db operation
-- 2 do the database operation
    3 new value =   get the latest db value
-- 4 compare and swap : set new value in cache, if current cache value == previous value
-- 5  if 4 succeeded , we are done
-- 6 previous value = current cache. Goto 3

2.3 Update cache using an updater thread

Any thread with a db operation like create , update, or even a read after a cache miss, does not directly update the cache.

Instead the request to update cache is put on a queue. Another thread reads the message one by one and updates the cache.

A disadvantage is that there is time delay before the updated value is available in  cache. Also in the case of cache misses, you might see multiple messages in the queue for the same cache update.

This is the preferred solution. If you can tolerate the time delay, it can eliminate race conditions and is easy to implement.

2.4 Versioning

We can steal ideas from MVCC which is used in database

The locking strategy in 1 locks both readers and writers.

We can improve on this by not requiring reads to locks.

Readers reads the latest snapshot value.
Writers lock not the value but a copy of the value. We allow only one copy additional writers will be blocked.
When the write is done with update ( commit), the updated copy is copied to the snapshot.

You can reduce the locking on writer even further by each writer his copy. Also assign say a version or transaction id to each copy. When a transaction commits, copy the value to snapshot.

3.0 Conclusion

In summary, consistency problems can arise due to multiple threads updating a cache and the backing database. Option 3 , updating the cache using a single update thread and fix these issues. This is a simple solution that will work for most scenarios. Option 2 is a non locking technique. Option 1 locking is the least scalable.Option 4 versioning is the most work to implement.      

Distributed Consensus: Raft

In the Paxos blog, we discussed the distributed consensus problem and how the Paxos algorithm describes a method for a cluster of servers to achieve consensus on a decision.

However the Paxos as a protocol is hard to understand and even harder to implement.

Examples of problems that need consensus are :

- Servers needing to agree on a value, such as whether a distributed lock is acquired or not.
- Servers needing to agree on order of events.
- Server need to agree on the state of a configuration value
- Any problem where you need 100% consistency in a distributed environment.

in a highly available environment.

The raft algorithm described https://raft.github.io/ solves the same problem, It is easier to understand and implement.

Overview

The key elements of raft  are:

Leader Election
Log Replication
Consistency ( in the spec they refer to this as safety)

A server in the cluster is the leader. All other servers are followers.

The leader is elected by majority vote.

Every consensus decision begins with the leader sending a value to followers.

If a majority of followers respond having received the value, the leader commits the value and then tells all servers to commit the value.

Clients communicate with leader only.

If a leader crashes, another leader is elected. Messages between leaders and followers enable the followers to determine if leader is still alive.

If a follower does not receive messages from the leader for a certain period, it can try to become a leader by soliciting votes.

If multiple leaders try to get elected at the same time, it is possible there is no majority. In such situations, the candidates try to get elected again after a random delay.

 In Raft, time is set of sequential terms. Term is time of certain length. Leadership is for a term.

2 Main RPC messages between leader and followers :

Request Vote : sent when a candidate solicits vote.
Append Entry : sent by leader to replicate a log entry

Scenario 1 : leader election cluster start up

let us say 5 servers s1 to s5.

Every server is a follower.

No one is getting messages from a leader.

s1 and s3 decide to become leaders (called candidates) and send message to other servers to solicit vote.
Servers always vote for themselves.
s2 and s4 respond to s1.
s1 is elected leader

Scenario 2 : log replication

A client connects to a leader s1 to set x = 3.

s1 writes x=3 to its log. But its state is unchanged.

At this point the change is uncommitted.

s1 sends appendEntry message to all followers that x =3. Each follower writes that entry to log.

Followers respond to s1 that change is written to log.

When majority of followers respond, s1 commits the change. It applies the change to its state so x is now 3.

Followers are told to commit by piggybacking the last committed entry, in the next appendEntry message. Followers commit the entry by applying the change to its state.

When a change is committed, all previous changes are considered committed.

The next appendEntry message from leader to followers will include the previous committed entry. The servers can they commit any previous entries they have not yet committed.

The cluster of servers has consensus.

Scenario 3 : Leader goes down

When a leader goes down, one or more of the followers can detect that there are no messages from the leader and decide to be come a candidate by soliciting votes.

But the leader that just went down has the accurate record of  committed entries, that some of the followers might not have.

If a follower that was behind on committed entries became a leader, it could force other servers with later committed entries to overwrite their entries. That should not be allowed. Committed entries should never change.

Raft prevents this situation by requiring candidate to send with the requestVote message the term and index of the latest message it accepted from the previous leader. Each Follower rejects requestVote with a term/index lower than its highest term/index.

Since a leader only commits entries accepted by a majority of servers and a majority of servers is required to get elected, it follows that a majority or a least half of the remaining servers have accepted that highest committed entry of the leader that went down.Thus a follower that does not have the highest committed entry from the previous leader can never get elected.

Scenario 4 : Catch up for servers that have been down

It is possible for a follower to miss committed entries either because it went down or did not receive the message.

To ensure followers catch up and stay consistent with leaders, RAFT has a consistency check.

 Every append entry message also includes term and index of previous message in leaders log. If it does not match in the follower, the follower rejects the new message. When AppendEntry is accepted by a server, it means leader and server have identical entries.


When a follower rejects an appendEntry, the server retries that follower with a previous entry. This continues until the follower accepts an entry. Once an entry is accepted, the leader will again send subsequent entries that will be accepted.

Leader maintains a nextIndex  for each follower. This is the index in the log that the leader will send to the follower next.

Scenario 5 : Cluster membership changes

Cluster membership changes refers to a bunch of servers being added or removed from the cluster.
May be even the current leader is no longer in new configuration.

This needs some attention because it is possible we end up with 2 leaders and 2 majorities.

Raft takes a two phase approach

First switch to joint consensus
   -- entries committed to servers in both configuration
  --  2 leaders one from each configuration
  -- majority from each configuration needs to approve stuff

Second switch to new configuration

Leader receives request to change configuration to new.
Leader uses appendEntry to send old,new config pair to followers
Once (old,new) is committed , we are in joint consensus period.
Leader then sends appendEntry for new configuration.
Once committed, new configuration is in effect.

Summary

Consensus is required when you need 100% consistency in a distributed environment that is also highly available. Raft simplifies distributed consensus by breaking the problem into leader election and log replication. Easier to understand means easier to implement and use to solve real world problems.

A future blog will go into an implementation.

References:

1. In Search of an Understandable Consensus Algorithm by Diego Ongaro and John Ousterhout Stanford University. https://raft.github.io/raft.pdf

Related Blogs:

1. Distributed Consensus : PAXOS

Distributed Systems : Basic Paxos

1.0 Introduction

How to build reliable highly available distributed systems that are consistent ? Paxos is a protocol that addresses this problem.

Paxos was authored by Leslie Lamport in his paper "Part time parliament"  and explained better in his paper "Paxos made simple". It has been implemented and used in many of the modern distributed systems built by Google, Amazon, Microsoft etc.

Consider a banking system with clients c1,c2 and server s.

c1 can issue command to s : add $200 to account A
c2 can issue command: add 2% interest to A

A single server can easily determine the order c1,c2 and execute the commands.

But if S crashes, no client can do any work. The traditional way to solve this problem is to fail over using standby. Another server S' , identical to S is standing around doing nothing. When S crashes, the system detects that S is no longer servicing requests, starts sending requests to S'.  For reasons that merit a blog of its own, fail over using standby is hard to implement , to test and more expensive. That will not be discussed here.

A second limitation is that when the number of client requests increase, the server may not be able to keep up and respond in a reasonable time.

The way to solve scalability and fail over issues is to have multiple servers says s1 and s2 servicing clients with replication between s1 ans s2, so the clients are presented with a single system view. Commands that execute on s1 due to its clients are also made to execute on s2 and vice versa, so that both s1 and s2 are in the same state.

In Figure 1 Both S1 and S2 are active and servicing clients.

Figure 1 : Multiple server cluster with replication

However a difference in the order of execution can lead to a consistency issue.

Assume account A has $1000
Say c1 connects to s1 invokes command subtract 200 from account A.
Say c2 connects to s2 and invokes command debit 5% interest to account A.

If the order of execution is c1,c2 then c1 increases A to 1200. c2 increases it to 1260. If the order of execution is c2,c1, then c2 increases A to 1250. c1 increases it to 1250. One server may have a value 1260 , while the other may have a value 1250.

To ensure consistent results, both servers need to agree on the order of execution. In other words, there needs to be consensus among the servers. You can have more then 2 servers and the same is true.

Paxos is a protocol for achieving consensus among a group of servers.

2.0 Paxos assumptions

A group of distributed servers communicating with each other.

Asynchronous communication

non byzantine : no devious unpredictable stuff

Messages can be lost.

A majority of servers are always available. So your system should have an odd number of server 3,5,7... so that a majority can be established. Common logic tells us that with an odd number of servers, any two majorities should have at least one overlapping member. This observation is critical to the correctness of the protocol.

Server can join or leave the system at any time.

3.0 What Paxos achieves

Reliable system with unreliable components.

Only one value may be chosen.

The value is chosen when it chosen by a majority of the servers.

Once a value is chosen, it cannot be changed.

This "one value" concept can be hard to understand for a first time reader. How is choosing just one value useful in solving any real world problems ? In reality , the value is likely to be a command that needs to be executed on the server. It could be command and data or both. Value is a simplification.

For this to be useful, the servers probably need consensus on not just one value but several values. That can be achieved by a minor extension to basic Paxos and will be discussed in a subsequent blog.

4.0 Actors in Paxos

Proposers propose a value to be chosen. Proposers are generally the ones handling client requests.

Acceptors respond to proposers and can be part of the majority that lead to a value being chosen.

Learners learn the chosen value and may put it to some use.

In reality, a single server may function as all 3 and this is what we will assume

5.0 The protocol

(1) Proposer proposes a value (n,v) where n is a proposal number and v is a value.

(2) If an acceptor has not received any other proposal, it sends a response agreeing to not accept
any other proposals with number less than n.

If proposal number is less than what it has accepted or agreed to accept, it can ignore the proposal.

If it has other lower number proposals accepted with value v or any other v', it responds with the accepted proposal number and value v'.

The acceptor continues to do this with any subsequent proposal it receives. It must remember the highest proposal number it has. This is important because as described in step 5, it should never accept any lower numbered proposals.

(3)The proposer examines responses to its proposal.

If majority of acceptors responded with value v', then mean that v' is either chosen or has a good chance of being chosen. Proposer must take v' as value . If majority does not have value, it can stay with original v.

(4) Proposer sends accept message (n,v')

(5) When an acceptor receives an accept message (n,v) or (n,v') . It must accept the value if n is still the highest proposal it has.

Between step 2 and 5, other proposers could have send other proposals with number higher than n. If the acceptor has any such proposals, it cannot accept n.

In either case, it returns to the proposer, the highest proposal number it has.

(6) The proposer can use the proposal number in response from 5 to determine if its accept message is accepted. If the proposal number is the same as n, then it known that n is accepted.

Other wise the returned number  is that of the larger proposal number that is around. It has to go back to step 1 and start with new proposal number greater than this.

6.0 Notes

Proposals have order as indicated by n. Newer proposals override older proposals. If an acceptor has received proposal n. It can ignore all proposals less than n.

Proposal numbers need to be unique across proposers. 

Multiple rounds of propose and accept may be necessary before a majority for a chosen value is reached.

Once a value is chosen, future proposers will also have to choose that value. That is the only way we can get to one and only one value chosen.

Proposals are ordered. Older ones are ignored or rejected

7.0 Examples

In this section we go through some scenarios of  how the protocol works.

7.1 Case 1 :  Value chosen for the first time 

Figure 2 : Value chosen for first time
 

This is the most basic case of no value yet chosen and a value proposed for the first time.

3 servers s1,s2,s3. 2 is majority

1. s1 sends proposal 1 with value X to s2
2. s2 has no previous proposal or value , so it responds agreeing to not accept any proposals numbers less than 1
3. s1 has agreement from majority. So it sends accept message to s2 which accepts and X is the chose value.



7.2 Case 2 :  Value proposed after one already chosen

Figure 3 : Value proposed after one chosen
s1 and s2 have agreed on value X.

s3 does not know of this. s3 send proposal 2 with value Y to s2.

s1 responds that it has accepted proposal 1 with value X.

s2 has to update its value to X. s2 sends an accept message with value X which is accepted.

s1,s2,s3, all have value X.

7.3 Case 3: No value yet chosen two competing proposals 1 wins

Figure 4: Competing proposals

There are 5 servers  s1,s2,s3,s4,s5. 3 is majority

s1 sends proposal 1 with value X to s2,s3
s2 agrees to accept 1,X
Before s3 receives accept message for  (1,X) s5 sends (2,Y) to s3,s4
Now s3 cannot accept (1,x) because its highest proposal is 2.
s3,s4 respond agreement to (2,Y) to s5
s3 ignores proposal (1,X)
s5 sends accept(2,Y) to s3,s4 which accept
s1 sends a new proposal (3,X).
s3 responds (2,Y)
s1 sends accept(3,Y)
s1 and s2 also agree on Y 


7.4 Case 4 : Not making progress or  liveness

Figure 5 : Not making progress

s1 proposes values to s2 ,s3. s5 proposes values to s3,s4.

s1 proposes (1,X). s3 agrees to not accept proposal less than 1.
Before (1,X) can be accepted s5 proposes (2,Y). s3 now agrees not to accept less than 2
When s1 tries to get (1,X) accepted, It will not get accepted because there is a proposal 2. It sends out a proposal (3,x).
s2 will not be able to get (2,Y) accepted because there is a (3,X). It sends outs a (4,Y).
s1 will not be able to get (3,X) accepted because there is a (4,Y). It sends out a (5,x)

This may go on and on. One way to avoid this is for each server to introduce a random delay before issuing the next proposal, there by givings the others a chance to get their proposals accepted.

Another solution is to have a leader among the servers and have the leader be the only one that issues proposals.

8.0 Paxos usage in real world 

The basic protocol enable servers to arrive at a consensus on one value. How does one value apply to real world system ? To solve real world problems like the one described in the introduction, you have run multiple instances or interactions of Paxos. For a group of servers to agree on the order of a set of commands, think of a list of command 0 .. n. Each Paxos instance would pick a command at each index. This is multi Paxos and merits a blog or discussion on its own.

Some real world usages of Paxos have been to arrive at consensus on locks, configuration changes, counters.

9.0 References

"Part time parliament" by Leslie Lamport
"Paxos made Simple" by Leslie Lamport
"Time, clocks and the ordering of events in a distributed system" by Leslie Lamport

JAVA 8 : Lambdas tutorial

Lambdas are the biggest addition to JAVA in not just release 8 but several releases. But when you look at the cryptic lambda syntax, like most regular programmers, you are left wondering why one should write code this way. 6. The purpose of this tutorial is to introduce lambdas, so that you can start using them in real code.

Overview

Lambdas facilitate defining, storing and passing as parameters blocks of code. They may be stored in variables for later use or passed as parameters to methods who may invoke the code. This style of programming is known as functional programming.

You might argue that JAVA already supported functional programming using anonymous classes. But that approach is considered verbose.

Example

Listing 1 shows the old way to pass executable code to a thread.

  public void Listing1_oldWayRunnable() {
        Runnable r = new Runnable() {
            @Override
            public void run() {
                System.out.println("Hello Anonymous") ;
            }
        } ;
        Thread t = new Thread(r) ;
        t.start() ;
    }

Listing 2 shows the new way using lambdas.

public void Listing2() {

        Thread t = new Thread(()->System.out.println("Hello Lambdas")) ;
        t.start() ;
    }

Listing 2 has no anonymous class.  It is much more compact.

()->System.out.println is the lambda.

Syntax

The syntax is

(type)->statement
 Where type is the parameter passed in. In our example, there was no parameter. Hence the syntax was ()->statement

If you had multiple parameters, the syntax would be
(type1,type2)->statement

If you had multiple statements, the syntax would be a
(type) ->{statement1; statement2} ;

Storing in a variable

The lambda expression can also be stored in variable and passed around as shown in listing 3.

 public void Listing3() {
        Runnable r = ()->System.out.println("Hello functional interface") ;
        Thread t = new Thread(r) ;
        t.start() ;
    }

Functional interface

JAVA 8 introduces a new term functional interface. It is an interface with just one abstract method that needs to be implemented. The lambda expression provides the implementation for the method. For that reason, lambda expressions can be assigned to variables that are functional interfaces. In the example above Runnable is the functional interface.

You can create new functional interfaces. They are ordinary interfaces but with only one abstract method. @FunctionalInterface is an annotation that may be used to document the fact that an interface is functional.

Listing 5 show the definition and usage of a functional interface.

@FunctionalInterface
    public interface Greeting {
        public void sayGreeting() ;
    }

    public static void greet(Greeting s) {
        s.sayGreeting();
    }

    @Test
    public void Listing5() {
        // old way
        greet(new Greeting() {
            @Override
            public void sayGreeting() {
                System.out.println("Hello old way") ;
            }
        }) ;

        // lambda new way
        greet(()->System.out.println("Hello lambdas")) ;
    }
}

Once again you can see that the code with lambdas is much more compact. Within an anonymous class, the "this" variable resolves to the anonymous class. But within a lambda, the this variable resolves to the enclosing class.

java.util.Function

The java.util.Function package in JDK 8 has several starter ready to use functional interfaces. For example the Consumer interface takes a single argument and returns no result. This is widely used in new methods in the java.util.collections package. Listing 6 shows one such use with the foreach method added to Iterable interface, that can be used to process all elements in a collection.

@Test
    public void Listing6() {
         List l = Arrays.asList(1,2,3,4,5,6,7,8,9) ;
         l.forEach((i)->System.out.println(i*i)) ;
    }
 
In summary, Java 8 lambdas introduce a new programming style to java. It attempts to bring JAVA up to par with other languages that claim to be superior because they support functional programming. It is not all just programming style. Lambdas do provide some performance advantages. I will examine them more in future blogs.

ConcurrentHashMap vs ConcurrentSkipListMap

In the blog Map classes, we discussed the map classes in java.util package. In blog ConcurrentHashMap, we ventured into concurrent collections and discussed the features of ConcurrentHashMap, which offers much superior concurrency than a conventional HashMap.

In this blog we discuss another concurrent map, the ConcurrentSkipListMap and compare it with ConcurrentHashMap. Package java.util has a HashMap and TreeMap. Have you ever wondered why java.util.concurrent has a ConcurrentHashMap, but no ConcurrentTreeMap and why there is a ConcurrentSkipListMap ?

In the non concurrent Collections, there is a  HashMap and TreeMap. HashMap for O(1) time complexity and TreeMap for maintaining a sorted order but O(logn) complexity. The implementation of a tree map is not a ordinary binary search tree(BST), because a BST that is not balanced degrades in performance to O(n) for input that is already sorted. TreeMap is implemented as a Red black tree, whose implementation is complex and involves balancing the tree (moving the nodes around) when nodes are added or removed.  The complexity is even more when you try to make the implementation concurrent (safe for concurrent use). For that reason there is no ConcurrentTreeMap in java.util.concurrent.

A concurrent implementation of SkipList is simpler. Hence, for a Map that is ordered and concurrent,the implementators choose SkipList.

What is a Skip List ?

A skiplist is an ordered linked list with o(log n) worst case search time. An ordinary linked list has o(n) worst case search time. A skip list provides faster search by maintaining layers of links, allowing the search to skip  nodes. As shown in the figure, the lowest layer is an ordinary linked list. But each higher layer skips some (more) nodes.

level4 10-------------------------------------100-null
level3 10-----------------50-----------------100-null
level2 10-------30------ 50-----70---------100-null
level1 10 -20 -30 -40 -50 -60-70-80-90-100-null

Let us you need to find 80 in the list.
Start are highest level 4. Search linearly to find the node that is equal to or whose next node is greater than 80. At level 4, 100 is greater than 80. So at node 10, move down to level 3.

At level 3, node 10, 50 is less than 80. Move to node 50. Next node 100 is greater that 50. Move down to level 2 at node 50.

At level 2 node 50, next node is 70 which is less than 80. Move to node 70.  Next node is 100 which is greater than 80. Move to level 1 at node 70.

At level 1, this is the last level. Keep going forward from 70 till you find 80 or reach end of the list.

Adding more levels can leads to faster search.

Skiplist has O(logn) performance for search, insert and delete. Depending on number of levels, it does use some extra space. Space complexity is O(nlogn).

In general, you will use a ConcurrentHashMap, if you must have O(1) for both get and put operations, but do not care about the ordering in the collection. You will use a ConcurrentSkipListMap if you need an ordered collection (sorted), but can tolerate O(logn) performance for get and put.

Lastly, SkipList is easier to implement than a balanced tree and is become the data structure of choice for ordered concurrent Map.

Apache Cassandra: Compaction

In Cassandra vs HBase, I provided an an overview of Cassandra. In Cassandra data model, I covered data modeling in Cassandra. In this blog, I go a little bit into Cassandra internals and discuss Compaction, a topic that is a source of grief for many users. Very often you hear that during compaction, performance degrades. We will discuss what compaction is, why it is necessary and the different types of compaction.

Compaction is process of merging multiple SSTables into larger tables. It removes data that has been marked for deletion and reduces fragmentation. Generally it happens automatically in the background, but can be started manually as well.

Why is compaction necessary ?

Cassandra is optimized for writes. A write is first written in memory to a table called Memtable. When Memtable reaches a certain size it is written in its entirety to disk as a new SSTable. SStable has an index which consists of sorted keys, which point to the location  in file that has the columns. SSTables are immutable. They are never updated.

The high throughput for writes is achieved by always appending and never seeking before writing . Updates to existing keys are also written to the current Memtable and eventually written to a new SStable. There are no disk seeks while writing.

Obviously, over time there are going to be several SSTables on disk. Not only that, but the latest column values for a single key might be spread over several SSTables.

How does this affect reads ?

Reading from one SSTable is easy. Find the key in the index. Keys are sorted. So a binary search would find the key. After that it is one disk seek to the location of the columns.

But as pointed out earlier, the updates for a single key might be spread over several SSTables. So for the latest values, Cassandra would need to read several SSTables and merge updates based on timestamps before returning columns.

Rather than do this for every read, it is worthwhile to merge SSTables in the background, so that when a read request arrives, Cassandra needs to just read from fewer SSTables ( one would be ideal).

Compaction

Compaction is the process of merging SSTables in order to

• read columns for partition key from as few SSTables as possible
• remove deleted data
• reduce fragmentation

We did not talk about delete earlier. When Cassandra receives a request to delete a partition key, it merely marks it for deletion but does not actually remove the data associated with the key. The term used in Cassandra is "tombstone". A tombstone is created. During compaction, tombstones are supposed to be removed.

Types of Compaction

Size tiered compaction:

This is based on number of  SSTables and size of table. A compaction is triggered when the number tables and their size reaches a certain threshhold. Tables of similar size are grouped into buckets for compaction. Smaller tables are merged into a larger table.

Some disadvantages of size tiered compaction are that read performance can vary because the columns for a partition key can be spread over several SSTables. A lot for free space ( double the current storage) is required during compaction, since the merge process is making a copy.

Leveled compaction:

There are multiple levels of SSTables. SSTables within a level are of the same size and non overlapping (Within each level, a partition key will be in one SSTable only) . SSTables in the higher levels are larger. Data from the lower levels is merged into SSTables of the higher levels.
Leveled compaction tries to ensure that most reads happen from 1 SSTable. The worst read performance is bound by the number of levels. This works well for read heavy workloads because Cassandra knows which SSTable within each level to check for the key. But more work needs to be done during compaction especially for write(insert)  heavy workloads. Due to the extra work to ensure a fixed number of SSTables, there is a lot more IO.

Data tiered compaction:

 Data written within a certain period of time say 1 hr is merged in one SSTable.  This works well when you are writing time series data and querying based on timestamp. A query such as give me columns written in the last 1 hr can be serviced by reading just 1 SSTable. This also makes it easy to remove tombstones that are based on TTL. Data with the same TTL is likely to be in the same SSTable and the entire SSTable can be dropped.

Manual compaction:

This is compaction started manually using the nodetool compact command. A keyspace and table are specified. If you do not specify the table, the compaction will run on all tables. This is called a major compaction. It involves a lot of IO and is generally not done.

In summary, compaction is really fundamental to distributed databases like Cassandra. Without the append only architecture, write throughput would be much lower. And high write through put is necessary for high scalable systems or stated in another way - writes are much harder to scale and are generally the bottleneck. Read can be scaled easily by de-normalization , replication and caching.

Even with relational databases, applications do not go to Oracle or MySql for every read. Typically there is cache like Memcached or Redis, that caches frequently read data. For predictable read performance consider fronting Cassandra with a fast cache. Another strategy is to use different Cassandra clusters for different workloads. Read requests can be sent to clusters optimized for read.

Lastly , Leveled compaction works better for read intensive loads where as Data tiered compaction is suited for time series data and when the there is steady write rate. Size tiered compaction is used with write intensive workloads. But there is no silver bullet. You have to try, measure and tune for optimal performance with your workload.


Related Blogs:

Cassandra vs HBase
Cassandra data model
Choosing Cassandra