Tag Archives: e-commerce

e-commerce

Travis Law

Travis’s Law:

Our product is so superior to the status quo that if we give people the opportunity to try it, they will defend it and demand its right to exist.

Travis Law is a quote from a speech by Travis Kalanick founder of Red Swoosh and wider known Uber. In his CEO role at Uber, there have been quite some scrimmages with local governments and unions. This law is one of the strategies what Uber uses to influence the outcomes of these discussions. They have been pretty successful with it.

Will it work outside Uber?

There is one other company that comes to mind that faces similar discussions with unions and governments that has used this approach successfully: Airbnb.

Some background

In a Havard Business Review article on Uber (and it’s assumed illegality) it is stated:

What’s more, Uber’s most distinctive capabilities focused on defending its illegality. Uber built up staff, procedures, and software systems whose purpose was to enable and mobilise passengers and drivers to lobby regulators and legislators — creating political disaster for anyone who questioned Uber’s approach.

Related laws

In a way, this is an extension to what Peter Thiel stated in Zero to One:

As a rule of thumb proprietary technology must be at least 10 times better than its closest substitute in some important dimension.

Where Thiel focusses on the product development part, Travis goes from the demand. The demand from users that is used to overcome obstacles, like protectionist rules and laws and unions acting to protect jobs and actually sometimes more investments and lobbies by unions.

web scale patterns in the bol.com back office – Mixed SQL – NoSQL

In the previous weeks, we started a series of blog posts that show you how we use “web scale” patterns to achieve scalability and flexibility in our back office software. The previous patterns discussed were Event Sourcing and CQRS. This week we will dive into mixed SQL – NoSQL. Showing you how this doesn’t just solve a technical problem, they help us solve our business problems!

Mixed SQL – NoSQL

Where needed in our services we are moving away from pure SQL. We create a mix with other types of storage. So we are using NoSQL (Not only SQL). One could also call this polyglot persistence. The notion that your application can write to or query multiple databases or one database with multiple models. It uses the same idea as polyglot programming. This expresses the idea that applications should be written in a mix of languages to take advantage of the fact that different languages are suitable for tackling different problems.

RTN – Billing platform for our retailers

RTN stores all kinds of transactions to charge and pay partners in our LvB (Fulfilment by bol.com) operations. In this part of our operation, we store and fulfil products in our warehouses for retailers that sell their goods on our platform.

The transactions to create invoices for our LvB partners stems from a number of services. We have all kinds of different attributes we want to account for to know why decisions have been made and for auditing purposes. These attributes depend on the transaction type. It was decided that the attributes wouldn’t be a part of the transactions table since they are only filled for a part of the records.

To accommodate for the attributes that depend on the transaction type we created an additional column in the table that is able to store key-value pairs in JSON format. The use of a pure SQL solution would have resulted in a weak design. As would the use of a pure NoSQL. In these cases, they work great together.

FNK – Warehouse orders

FNK processes our customer orders to create warehouse orders. It determines the warehouse that will fulfil the customer demand and instructs the warehouse to fulfil the order. Besides regular warehouses, it also communicates with our warehouse for digital products (e-books and software downloads) and with retailers that sell their products on our platform and take care of the fulfilment themselves.

These retailers have requirements that differ from the other warehouses. To accommodate these while avoiding a to a specific part in this services we introduced an additional column that stores XML. This mixture of SQL (one table for all warehouse orders) and NoSQL (stored XML) results in a simple model that can handle requirements that are only needed for a part of the orders. Since the data in the XML is hardly needed in this service but mostly in downstream services, there are no drawbacks on performance.

What we learned

The NoSQL parts in these mixed data stores are mostly used to read from. If you need to specifically filter on these of have a requirement to use them in joins performance will degrade.

Next in web scale patterns in the bol.com back office

In the next week’s episode on the following subject will be published:

  • Micro services
web scale patterns in the bol.com back office

web scale patterns in the bol.com back office – Event Sourcing

Last week we started a series of blog posts we show you how we use “web scale” patterns to achieve scalability and flexibility in our back office software. Last week’s pattern we discussed was CQRS. This week we will dive into Event Sourcing. Showing you how this doesn’t just solve a technical problem, they help us solve our business problems!

Event Sourcing

The idea of Event Sourcing is that every change to the state of a system is captured in sequence and that these events can be used to determine the current state. Consequently, the state of the system for any point in time can be determined by replaying the events. The structure of the service changes from storing state to storing events.

The most obvious that we gain by using Event Sourcing is that we have a log of all the changes. We can see everything that happened. This enables us to:

  • Do a complete rebuild;
  • Determine the state of the system at any point in time;
  • Event replay – Compute the consequences of a change in a past event of recalculate the consecutive states based on the proper sequence of events (in case messages in an asynchronous communication weren’t received in the proper order).

Using Event Sourcing can feel a little bit awkward for some developers. However, it offers a variety of opportunities. One could replay the events on a test environment to see exactly what happened on pro, while you have the ability to stop, rewind and replay the events running a debugger. This provides also a way to do parallel testing before promoting an upgrade to production.

Where do we use it at bol.com?

web scale patterns in the bol.com back officeOne of the examples where we use Event Sourcing is Condition Management and especially the calculations of accruals and invoices for (purchasing) conditions. A large set of our purchasing conditions is based on either purchasing amounts or values and sales amounts and values. In general these purchasing conditions have to attributed to (sets of) single products, product categories, suppliers and brands.

Storing the events that represent the purchase and sales of goods allows us to implement functionality that would be very hard to develop if we wouldn’t. Typically a purchasing condition isn’t agreed with a supplier of a brand on the first of January. While it could be valid from the first of January. The Event Sourcing model allows us to handle conditions that are entered into the system somewhere in March or April that are valid from the first of January. These conditions will be handled by passing all the events from the start date and the appropriate accruals and invoice can be created.

With the Event Sourcing model, we are also more loosely coupled to the source services for purchasing and sales. Our calculations can handle events that are captured out of sequence or even very late. Condition values are still calculated properly and handled as accounting and controlling have prescribed.
For the future, we are planning to implement scenario run through and comparisons. This would support our buyers while negotiating with suppliers.

Next in web scale patterns in the bol.com back office

In the next week’s episodes on the following subjects will be published:

web scale patterns in the bol.com back office – CQRS

web scale patterns in the bol.com back office – CQRS

In this series of blog posts, we show you how we use “web scale” patterns to achieve scalability and flexibility in our back office software. We will guide you through how we apply patterns like CQRS, event sourcing and micro services to solve puzzles in our back office services. These patterns don’t just solve a technical problem, they help us solve our business problems!

We need web scale in the back office since more and more functionality from the back office is needed on the web site to offer better service to our customers. For example, more parts of our web shop do request on our stock levels and warehouse configuration to determine how fast product can be delivered to our customers and with what options. Consequently, the services that know our stocks levels and warehouse configuration also have to be scaled to handle these volumes. To enable this we don’t just need more hardware, we also need to apply patterns to our services to create a proper structure.

CQRS

CQRS is short for Command Query Responsibility Segregation. At the core of CQRS is the notion that a different model can be used to alter data than the model that is used to query data. Updating and reading information have different requirements on a model. There are enough cases where it serves to split these. The downside of this separation is that it introduces complexity. So this pattern should be applied with caution.

The most common approach for people to interact with data in a service or system is CRUD. Create, Read, Update and Delete are the four basic operations on persistent storage. The term was likely popularised by James Martin in his 1983 book Managing the Database environment. Although there exist other variations like BREAD and MADS, CRUD is widely used in systems development.

When a need arises for multiple representations of information and users interact with these multiple representations, we need something that extends CRUD. This because the model to access the data tends to be split over several layers and becomes overly complicated.

What CQRS adds

CQRS introduces a split into separate models for update and display, Command and Query respectively. The rationale for this is that for many problems in more complex domains having the same model for commands and queries leads to a more complex model. A model that does neither well.

Where do we use it at bol.com?

One of the examples of where we use CQRS in the back office services at bol.com is in our Inventory Management. Inventory Management handles all updates on stock levels and serves them to several services in out landscape including our web shop.

The updates of stock levels come from our warehouse management and include reservations based on customer orders, shipments and received goods. The queries on the stock level originate in the web shop, check out and fulfilment network. As you can imagine these queries have quite a different profile compared to the updates. Besides that, the number of queries far outreaches the number of updates.

Given these different requirements we decided to split command (updates) and query for inventory management. All updates are handled by a technically isolated part of the service. Stock levels are served by other services by another isolated part.

Implementation

web scale patterns in the bol.com back office – CQRSThe part that handles the updates has several models. The incoming changes like the shipments and received goods have to be handled in for example stock mutations, stock levels and stock valuation. These models receive updates and process them to a new stock level and stock valuation. Once a new stock level is calculated, it is published on a messaging queue to the query part. This message is also consumed by other services that need these.

The query part is a simple single table. The messages from the update part are stored in this table and there is no additional logic or processing. Queries from other services are handled by a REST interface. Due to this design, this call has a very high cache hit ratio. Which of course leverages performance.

Next in web scale patterns in the bol.com back office

In the next week’s episodes on the following subjects will be published:

Presentatie op LAC congres – Agile schalen op basis van best practices

lac-agile-schalen-op-basis-van-best-practicesOp donderdag 17 en vrijdag 18 november is de 18e editie Landelijk Architectuur Congres. Net als vorig jaar ga ik in de track Agile Architecting een presentatie geven over architectuur en architecten in een Agile omgeving. De titel van de presentatie dit jaar is: Agile schalen op basis van best practices.

Bij bol.com hebben we jarenlange ervaring met het werken met agile en scrum. Het aantal IT teams dat hiermee werkt is de laatste 2 jaar enorm sterk gegroeid. Daarnaast doen we diverse experimenten met holacracy. Voor het opschalen zijn we steeds op zoek gegaan naar best practices. Je zal dan ook veel elementen uit SAFe terug zien, maar nooit SAFe.

Agile schalen op basis van best practicesIn de meer dan 100 scrum sprints die we er bij bol.com inmiddels op hebben zitten hebben we een berg ervaring opgedaan met agile architectuur en het schalen van agile practices. Architectuur kan een belangrijke bijdrage leveren aan een snellere time-to-maket. In de presentatie zullen voorbeelden gebruikt worden uit bijvoorbeeld de realisatie van sneller en vaker leveren, de winkel langer open en Logistiek via bol.com.

Sneller en vaker leveren

Onze winkel gaat sneller en vaker leveren. Zoals je in de afbeelding kan zien is het maar een kleine aanpassing in de front-end/website. Zoals met veel fulfillment aanpassingen zit er een hele wereld van planning en operatie achter om dit ook daadwerkelijk voor elkaar te krijgen.
Sneller en vaker leveren

Sneller en vaker leveren maakt het met de al bestaande leveropties voor klanten mogelijk om de levering van bestellingen af te stemmen op hun behoefte.

Global Innovation Index 2016

The Global Innovation Index (GII) 2016 is an annual publication which features a composite indicator that ranks countries/economies in terms of their enabling environment to innovation and their innovation outputs. The GII covers 141 economies around the world and uses 79 indicators across a range of themes. The Global Innovation Index 2016 was created by Cornell University, INSEAD, and the World Intellectual Property Organization (WIPO). The theme of the 2016 Global Innovation Index (GII) is ‘Winning with Global Innovation’.

Here is an overview of the indicators that are used to create the innovation index and how they are related:
Global Innovation Index factors

And this is how the measures are calculated:

  • The Global Innovation Index is the simple average of the Input and Output Sub-Indices.
  • The Innovation Efficiency Ratio is the ratio of the Output Sub-Index over the Input Sub-Index.
  • The Innovation Input Sub-Index is the simple average of the first five pillar scores.
  • The Innovation Output Sub-Index is the simple average of the last two pillar scores.

Global Innovation Index Ranking

Here is the 2016 ranking for the Global Innovation Index. Switzerland, Sweden, the United Kingdom (UK), the United States of America (USA) and Finland are the world’s five most-innovative nations:

  1. Switzerland
  2. Sweden
  3. United Kingdom
  4. United States of America
  5. Finland
  6. Singapore
  7. Ireland
  8. Denmark
  9. Netherlands
  10. Germany

The Netherlands falls five ranks to 9th place, mostly driven by an FDI-related (Foreign Direct Investment) variable and missing data points.

The GII rankings have shown a remarkable level of global diversity among innovation leaders over the years. Among the top-ranked 25 innovative nations this year are not only economies from Northern America (such as Canada and the USA) and Europe (such as Germany, Switzerland, the UK and the Netherlands) but also from South East Asia, East Asia, and Oceania (such as Australia, Japan, Korea, and Singapore) and Northern Africa and Western Asia (Israel).

The distance between the performance of the top 10 ranked innovation nations and all others is still wide. The innovation divides remains in 2016 according to the GII 2016.

The Netherlands in the Global Innovation Index

The Netherlands has been ranked in the top 10 economies of the GII since 2008. It’s fall on the ranking this year is largely because of methodological considerations (see below). This year its ranking is affected by its lower ranks on both the Innovation Input Sub-Index (12th) and the Innovation Output Sub-Index (9th).

The Netherlands achieves a top 25 ranking among all economies for all pillars of the GII, with a better ranking this year in Infrastructure (12th) and Business sophistication (9th). Conversely, the Netherlands’ performance falls at the pillar level in Knowledge and technology outputs, where it ranks 16th overall. This change is mainly a consequence of lower rankings in the Knowledge diffusion sub-pillar (114th) and the indicator FDI net outflows (118th).

The latter indicator, identified as highly volatile in previous GII editions, partly drives the fall in the ranking of the Netherlands. Also, for some new variables—namely, IP receipts and ICT services exports — the Netherlands lacks data.

Building the next generation webanalytics solution

At this year Berlin Buzzwords our colleague Niels Basjes presented out next generation webanalytics solution. Internally this solution is called measuring 2.0.

To help the customers find what they want in our web shop we want to serve personalized content. To do this we need to understand what products/promotions we showed them and which of those made them happy. However in the fine grained personalization that has been introduced over the last few years we see that just measuring ‘what page’ we showed – like all the standard web analytics systems do – is no longer enough. So we need something different. In order to get a solution that will support our business for the coming years we raised the bar to the top: Measure everything and analyse in near-realtime.

Next generation webanalytics solution

Here is the video of Niels’s presentation:

The slides are here: next generation webanalytics solution.

Global Competitiveness Report 2015-2016

The World Economic Forum (WEF) published its Global Competitiveness Report, a comprehensive assessment of economic competitiveness across the globe. Each country’s relative economic strength is determined by analysing twelve pillars–including the capacity to innovate, infrastructure, and health factors. The top five is:

  1. Switzerland
  2. Singapore
  3. United States
  4. Germany
  5. The Netherlands

These results shouldn’t be a surpise if you are familiar with Global Innovation Index 2015 (GII), Digital Economy and Society Index (DESI) 2015 or for example the Bloomberg Innovation Index

Global Competitiveness Report – Interactive graphic

Over at Quartz they created an interesting interactive graphic based om the Global Competitiveness Report 2015-2016. Very illustrative although they restricted the factors to these seven:

 Global Competitiveness Report

  • Higher Education and Training
  • Internet Users
  • Public Institutions
  • Capacity for Innovation
  • Soundness of Banks
  • Life Expectancy
  • Total Tax Rate