Service Meshes + Kubernetes: Solving Service-Service Issues

Saturday 23rd March Kubernetes Day India 2019 event is happening in Bangalore. Mr. Ben Hall has arrived here, in Bangalore as speaker for this event. He is founder of The "DigitalOcean Bangalore" Meetup group, grabbed this opportunity and organized meetup event "Service Meshes + Kubernetes: Solving Service-Service Issues" today evening, where MR. Ben Hall shared his knowledge. Here is my notes exclusively for readers of this blog "Express YourSelf !"

Digital Ocean announced about another event on April 4th and encourage all to participate. They also promote new offering about managed Kubernetes on Digital Ocean. 

Kubernetes is great tool. However we need service mesh for security, scaling, communication among pods. It is about TLS, certification roations Auth/Security, Rate Limit, Retry Logic, Fail Over etc. It provides more control for A/B testing, Canary releases, collecting system metrics and to verify, is this trusted caller pod? The service mesh can be implemented using: 

  • linkerd
  • HasiCorp Consul
  • istio

Istio provides four key capabilities
1 connect
2 secure. e.g protect the system against fake payment service
3 control
4 observe

Istio adds/extends some more capabilities to kubernetes APIs, by adding YAML files. Grafana and Prometheus installation is part of istio installation. 

istio is all about just configuring Envoy proxy. Istio uses three major components. 1. Pilot 2. Mixer and 3. Citadel

He demonstrated istio on his own website / learning platform katacoda. He testing using curl to generate ingress traffic. He also mentioned and demonstrated "scope" tool. It is for monitoring, visualization and management for Docker and k8s. It is not part of istio. 

We had interesting QA sessions.

* Yes, istio is adding little latency, when we use HTTP 1.0 based RESTful APIs. However, we get performance gain, when we use gRPC or HTTP 2.0 based RESTful APIs. It is tradeoff between, performance at production environment for given hardware, or gain in terms of developer's productivity. 

* Prometheus  is used to store all matrices of cluster. 

* How to configure different time out values for 2 different consumers, who consumes the same service? Well, we can duplicate service with different names, or modify some application level logic. 

* for pod communication, one can use either RPC (gRPC, RESTful API) based or enterprise service bus based (message queue and kafka). If one takes the second approach, then istio may or may not provide additional values. It depends. 

At the end we had tea and light snacks of cookies and Samosa. 



This blog post is NOT verbatim of the speech. I captured this note as per my understanding. It may not necessarily indicate the speaker's intention. So corrections/suggestions/comments are welcome.

MicroService Structure

1. API

* Operation
- Command to modify data
- Query to retrieve data

* Types
- Synchronous
- Asynchronous 

* Protocol
- gRPC
- RESTful

Here the microservice is like server. Customer will invoke the service using API

2. API client

Here the microservice is like client. It will invoke another microservice using its API. 

3. Event Publisher

4. Event Consumer

Event is typically DDD event.  

5. Business logic

6. Private Database

MicroServices : common characteristics

- Automated Deployment

- Componentization via Services : 
-- component is a unit of software that is independently replaceable and upgradeable.
-- A service may consist of multiple processes that will always be developed and deployed together
-- services are independently deployable.
-- Microservices have their own domain logic

- Organized around Business Capabilities
-- No 3-tier

- Products not Projects

- Smart endpoints and dumb pipes
-- microservices aim to be as decoupled and as cohesive as possible
-- Microservices receiving a request, applying logic as appropriate and prois ducing a response
-- No Enterprise Service Bus (ESB), with sophisticated facilities for message routing, choreography, transformation, and applying business rules.

- Decentralized Governance
-- difference component, different langugae
-- Patterns: Tolerant Reader and Consumer-Driven Contracts

- Decentralized Data Management
-- Domain-Driven Design DDD divides a complex domain up into multiple bounded contexts and maps out the relationships between them.
-- Polyglot Persistence : Each service owns its database
-- Results in simpler upgrade of application. 
1. User sessions : Redis
2. Financial Data and Reporting : RDBMS
3. Shopping Cart : Riak
4. Recommendation : Neo4j
5. Product Catalog : MongoDB
6. Analytics and User activity logs : Cassandra

- Infrastructure Automation
-- CI/CD Pipeline

- Design for failure
-- The application should able to tolerate the failure of services
-- Real time monitoring (of circuit breaker status, current throughput and latency) and auto restore of services.

- Evolutionary Design
-- How to divide monolith : 
1. The key property of a component: independent replacement and upgradeability
2. drive modularity through the pattern of change: Most frequent changed code should be in one service. Least frequent changed, stable code in another service. The modules often changed to gather should be merged in single service. 
-- Avoid using versing of services
-- Example The Guardian website


Python Lambda


  • Lambda is a special type of Python function that have limited capabilities.
  • A function that will be used only once in your program. These functions are called anonymous or unbound functions.

lambda arguments: expression

  • Lambda functions can accept zero or more arguments but only one expression
  • Here return statement is implicit.

What kind of things can I, and can I not, put into a lambda? 

Don't or Limitations

  • If it doesn’t return a value, 
  • If it isn’t an expression and can’t be put into a lambda
  • You can only use expressions, but not statements.
  • lambda function with multiple lines/expressions/statements is not possible
  • You cannot declare and use local variables.
  • If you can imagine it in an assignment statement, on the right-hand side of the equals sign, it is an expression and can be put into a lambda.
  • lambda can be member of list, dictionary 
  • You can only use one expression.

Use cases

1. map and reduce Python built-in function
2. filter Python built-in function
3. sort and sorted Python built-in function
4. Print formating
5. Use in WhLambda functions can accept zero or more arguments but only one expressionile statement. 
6. if for loop is invoking a function, then it can replaced with map. 

Multiple expressions

Use tuples, and implement your own evaluation order
If you are a coward and fear that evaluation order will change in a future release of python, 

you can use eval

You can also use apply
map(lambda x:apply(x,()),(f1,f2,f3))

General Notes
  • One can pass functions as arguments to other functions
  • A function can be the return value of another function.
  • The expression returns (or evaluates to) a value, whereas a statement does not. 

Python itertools module


itertools is a collection of “fast, memory efficient tools”. If you have not used "itertools" module, then most likely you might have written code, that was unnecessary. Its name is itertools as, it consists of functions made using iterator building blocks. All of the functions in the itertools module have yield keyword. Remember ? Every generator is an iterator but not every iterator is a generator.


to slice list like [start:stop] we can optionally pass step also. If we pass only one argument then it is value for stop and start = 0 by default. If you want to specify start, then you must specify stop. to indicate stop = 'till end', use None as stop value  


Here default is sum (for int) and concatenation (for string). Default function : "operator.sum". One can pass custom function also. E.g. operator.mul. 
Example: add new item and repeat last two items. So each item will be repeated 3 times. 

def f(a, b):
    print(a, b)
    return a[-2:] + b

def fn_accumulate():
    print(list(itertools.accumulate('abcdefg', f)))

This can be written in better way, using second order recurrence relation, as mentioned in

permutations = npr
combinations = ncr
combinations_with_replacement = ncr + pair with self

For all above three functions, r can be passed optionally. 
product to generate Cartesian product. It can be used to generate binary numbers. 
map(list, itertools.product("01", repeat = 5))


merging two lists, quicker. it can use to add anything as prefix in list. As suffix in list. 

Here we need to first put all iterables in a list and then pass the list (iterables )

similar to zip. Add 'None' for unpair items. We can also pass fillvalue to replace None with something else. 

we can use zip to add index number to all. 

for i in zip(count(1), ['a', 'b', 'c']):
    print i

Infinite operator

count is an infinite iterator 
To stop (1) use if condition OR (2) use islice
optionally step can be passed to count. This step can be fractions.Fraction too. 

cycle is also infinite iterator. It will cycle through a series of values infinitely
To stop use if condition

repeat takes object as input, instead of itertable. 
To stop pass optional argument 'times'


All functions in filter category takes predicate function as an argument. 

Here second argument is list of Booleans. Accordingly the first argument will be compressed, i.e. absent or present. It is like masking with boolean & (AND) operator.  Here the Boolean list can be output of cycle to create a pattern. Note: these values in list are consider as False. 0, False, None, ''
For example

def fn_compress():
    mask = [1, 0, 0]
    res = itertools.compress(range(20), itertools.cycle(mask))
    for i in res:

instead of list of Booleans, we can pass predicate as first argument here. Here if predicate return true, then drop. if predict return false then do not drop + disable predict. 

it is opposite of dropwhile. Here if predicate return true then take. if predict return false then do not take + siable predict. 

Same as dropwhile. Here the predict will not be disable. 
It will return all values where predicate function returns false. It takes predicate function and itertable as input. This is opposite to built-in-function filter.

We can use built-in function filter, to list out all the factors of given integer and find out prime or not also. 


if sort tuple, then it will be sorted as per first element. Then if use groupby for this sorted tuple then it will return itertato of "key , iterator" combinations. Here key is first memeber of tuple, which repeat  in several tuple. and iterator is list of all tuples, where that perticular key is used. 

This can be used to remove duplicate character in string 
>>> import itertools
>>> ''.join(ch for ch, _ in itertools.groupby(foo))

groupby will not do sorting. So if we call without calling sorted then it will group by only locally. As such no use of groupby without calling sorted. in sorted we can pass key.

sorted function by default sort on first value of tuple. This can be change by passing
1. key = lambda x: x[1]
2. key = operator.itemgetter(1)


It is similar to map. Here, we can pss list, or, list of tuple. We can use to convert co-ordinate systems, Cartesian to Polar and vica-versa. with map function, we can pass two list. Those two list should be zip and pass as list of tuple in starmap. for map if we want to pass constant in stead of one of the iterators, we can use repeat from itertools. The map can also use to create list of objects, where constructor is passed as function. 

it will generate multiple independant iterators. It is like copy function. It can also create multiple and identical tuples (iterators) from a string. Default = 2 copies. When you call tee() to create n independent iterators, each iterator is essentially working with its own FIFO queue.

Possible software using itertools

1. Veda Chanting pattern. GHAN - PAATH etc.
2. Convolution coding.
3. Turbo coding
4. Gray code generator. 
5. Fibonacci series. 



Github : similar tools

Jupiter Notebook

Official Documentation