🎨 Architecting a privacy-aware render server

Photo by Sergey Zolkin on Unsplash

This is part 3 of my series on server-side rendering (SSR):

  1. 🤷🏻‍♂️ What is server-side rendering (SSR)?
  2. ✨ Creating A React App
  3. [You are here] 🎨 Architecting a privacy-aware render server
  4. 🏗 Creating An Express Server
  5. 🖥 Our first server-side render
  6. 🖍 Combining React Client and Render Server for SSR
  7. ⚡️ Static Router, Static Assets, Serving A Server-side Rendered Site
  8. 💧 Hydration and Server-side Rendering
  9. 🦟 Debugging and fixing hydration issues
  10. 🛑 React Hydration Error Indicator
  11. 🧑🏾‍🎨 Render Gateway: A Multi-use Render Server

This week, we are back to our adventures in server-side rendering (SSR). So far in this series, we have looked at:

At this point in our journey into SSR, we need a server. Before we make a server, we must consider privacy and performance so this post is going to be light on code as we talk through important considerations. Though these considerations can be dealt with later, thinking about them upfront will help us avoid some really big pitfalls.

📈 Performance

All our initial considerations regarding server architecture come down to performance. Performance is the reason we want to implement SSR. We want to reduce the time our users must wait to see and interact with our site. Since the render of a page, whether in a browser or in our render server, can take variable amounts of time based on network latency, data requirements, and more, this inevitably means caching. Caching also helps us reduce costs since the render server will do less work. Whether caching is provided by your own implementation or via a service like Fastly, it has implications on what we should render on the server.

  • To get the most from our SSR solution, we need to render as much of a page as we possibly can.
  • To get the most from our caching, we want to share renders among as large a user base as we can so that a single render provides a cache hit for many people.

These two points compete against one another. In an ideal SSR world, for any given route the entire page is rendered so that the client does as little work as possible, but in an ideal caching world, a single cached entry for a route is shared with every user so that it is rendered once and then just shared. Assuming our app changes the page based on information about the user, both general – what device they are on, whether they are logged in or not, etc., and specific – what their name is, where they live, etc., we only get the most from caching if we do not render differentiating information during SSR such that we can share that render with more users. I doubt there is a hard and fast rule about how to find that caching sweet spot between being too specific about cache keys or being too general about rendering, but we can make things easier for ourselves.

First, we must ensure the cache key only includes generalizations and non-user-specific info.

Second, we introduce means to make generalizations about the user that can be a part of the cache key (is logged in, using Android, etc.) and that we can then pass to the render server to influence what is rendered.

Combining these two pieces of generalizing users and using that to influence the cache keys enables us to maximize what we can render while providing cacheable results that can be shared without leaking personal information. However, since the generalized information must be calculated and it can influence the cache key, there are implications for our architecture; if the render server does the generalization work, how can it tell the cache about it? We will look into that later, but for now, let's just note it as a problem to be solved.

📊 Data Access

At this point, we have worked through how to architect our server to support caching via generalizing users so that cache hits are likely without leaking personal information. That's great, and in fact, one might think, unnecessary. As long as user-specific information is not available to the render server, we cannot possibly render user-specific data. In many apps, data is obtained via REST or GraphQL and as such, is asynchronous. Since our render server is only rendering the very first render of a page, any asynchronous data will not be resolved and therefore not included in the result. So, where's the problem?

Well, not all data is asynchronous; some may be in the request itself, and even if we don't cache based on it, we must still ensure it is not rendered in the result. To mitigate this, we can use frontend components that wrap access to user-specific information so that we have a consistent rendering experience. Whether rendering for the first time in the client or the first time on the render server, these components will ensure the right thing is rendered and that the client will render the real data. Such data access components might render a spinner or some other placeholder that represents the currently unusable personal information (such as username) until the data is available. This means that all the considerations for rendering the right thing, whether in a browser or in the render server, are codified within the React app itself.

By using components in conjunction with linters and tests, we can enforce a data access policy that enables us to SSR effectively while respecting and enforcing user privacy.

📃 In Summary

That's a lot of words. Hopefully it has clarified some of the more jagged edges to SSR that often do not seem apparent until much later. It is much easier to make considerations about these things up front1.

In discussing how our render server will work with respect to a cache and user privacy, we have uncovered some details that affect not only the render server, but also our React app:

  1. The render server should sit behind a cache for performance, cost, and other benefits
  2. User-specific data like names, location, etc. should be omitted from the SSR result, cache keys, and initial client-side render
  3. It is useful to allow generalized user information in SSR, but it must also form part of the cache key
  4. Using React components, linters, and tests can enforce your policy around rendering user-specific content

That is it for this week. I thought I would get around to implementing the first version of our server, but I did not, so we will get into that next time. In the meantime, here are some things to consider that we will likely address in upcoming posts.

  1. Given that SSR only performs the very first render of our React app, how can we include asynchronous data and render more of our page?
  2. How will our render server know what browser code to render?
  3. How can generalizations about users be included in the cache key and used by the render server?
  1. something I have painfully learned by not doing so []

Monitoring My Blog Using Uptime Robot and IF

A week or two ago I discovered that my blog was not loading and I had no idea why it was throwing the 500 error code nor for how long it had been doing so. Having experienced this once or twice before, I went into my administration dashboard, stopped the website and application pool, then started them again. This fixed the immediate issue and my blog was back online, but I was not satisfied. I no longer wanted to discover this issue by chance so I went looking for ways to monitor my site.

I found several methods that could help, including one that uses my site's RSS feed as an IF trigger on IFTTT1, but I did not like this approach, so I looked around a little more. Eventually, after reading over a few options, I settled on using Uptime Robot. Uptime Robot allows up to 30 monitors on their free tier, which can be monitored at various frequencies down to every five minutes (if you want more monitors that are checked more frequently, you can look at their various paid options). Using this service, I not only will find out if my site goes down, but I also get stats over time on the reliability of my site.

Setting up a monitor on an HTTP(s) URL
Setting up a monitor on an HTTP(s) URL

Setting up a monitor was really easy and a quick test resulted in an email telling me the site was down, followed by another telling me it was back up once the site was restored. This was great although I felt an email was not enough. While Uptime Robot provides SMS support for sending alerts, they also provide you with an RSS feed on your account that syndicates your uptime alerts. Using an IF rule and the IF app on my phone, I was able to set up phone notifications for when my blog transitioned state between being up and down.

My Settings provides link to RSS feed for monitors
My Settings provides link to RSS feed for monitors

Trigger settings for IF rule to send notification to phone
Trigger settings for IF rule to send notification to phone

I retested the monitor (this meant taking the site down and waiting until the next monitor cycle) and convinced myself that the IF trigger and action were working satisfactorily. Now, whenever my blog experiences a glitch, I will know within about five minutes or so. Not only that, but if it fixes itself before I get chance to do so, I will have some stats that I can use to determine if there is a fundamental issue with my site's up-time. Uptime Robot provides a dashboard for managing monitors and viewing stats.

Uptime Robot dashboard view
Uptime Robot dashboard view

There is also a "TV Mode" for showing live stats, should you want a more permanent display in your office, for example. All of these views have a responsive layout, making it easy to check statuses from a mobile device.

Uptime Robot TV Mode
Uptime Robot TV Mode

Since setting the monitor up, my site has been down a lot. I do not know for sure if this is more or less than usual because I was not monitoring it this closely before, but I learned that my hosting provider has been updating servers recently. These hardware changes have caused all sorts of havoc with the reliability of site up-time for a lot of people, it seems2. Thankfully, due to both Uptime Robot and the responsiveness of my hosting providers support team, most issues were discovered and resolved in a reasonable time.

During these availability issues, I learned that just finding out when my site is down was not sufficient, so I added an additional "site back up" rule to IFTTT. This turns out to be really useful when your site is down while one is sleeping as it removes the need to go check if it the site is back up upon waking.

In Conclusion

While I am disappointed that my site was down, I was really happy to see that my Uptime Robot monitoring was doing exactly what I wanted. Not only that, but I have screen grab showing less than perfect stats, which makes for a great addition to this blog.

Overview from dashboard
Overview from dashboard

Uptime Robot is a nice discovery and a welcome addition to my suite of tools. The inclusion of a RSS feed to check monitor status as well as an API, which I am yet to explore, make it easy to integrate the information from Uptime Robot monitors into other tools.

  1. If This Then That: ifttt.com []
  2. I will refrain from going into detail on what I think about a company failing at their core purpose when doing something relating to that core business (feels a little like not serving food in a restaurant because they were buying new food) []

LINQ: Notation, Syntax, and Snags

Welcome to the final post in my four part series on LINQ. So far, we've talked about:

For our last look into LINQ (at least for this mini-series), I want to tackle the mini-war of "dot notation" versus "query syntax", and look at some of the pitfalls that can be avoided by using LINQ responsibly.

Let Battle Commence…

For anyone who has written LINQ using C# (or VB.NET), you are probably aware that there is more than one way to express the query (two of which, sane people might use):

  1. Old school static method calls
  2. Method syntax
  3. Query syntax

No one in their right mind should be using the first of these options; extension methods were invented to alleviate the pain that would be caused by writing LINQ this way1. Extension methods, static methods that can be called as though member methods, are the reason why we have the second option of method syntax (more commonly known as dot notation or fluent notation). The final option, query syntax, is also known as "syntactical sugar", some language keywords that can make coding easier. These keywords map to concepts found in LINQ methods and query syntax is what gives LINQ it's name; Language INtegrated Query2.

They all map to the same thing, a sequence of methods that can be executed, or translated into an expression tree, evaluated by a LINQ provider, and executed. Anything written in one of these approaches can be written using the others. There is often contention on whether to use dot notation or query syntax, as if one is inherently better than the other, but as we all know, only the Sith deal in absolutes3.  Hopefully, by the end of these examples you will see how each has its merits.

Why are LINQ queries not always called like regular methods?

Because sometimes, such as in LINQ-to-SQL or LINQ-to-Entity Framework, the method calls need to be translated into SQL or some other querying syntax, allowing queries to take advantage of server-side querying optimizations. For a more in-depth look at all things LINQ, including the way the language keywords map to the method calls, I recommend looking at Jon Skeet's Edulinq series, which is available as a handy e-book.

Before we begin, here is a quick summary of the C# keywords that we have for writing queries in query syntax: `from`, `group`, `orderby`, `let`, `join`, `where` and `select`.  There are also contextual keywords to be used in conjunction with one or two of the main keywords:`in`, `into`, `ascending`, `descending`, `by`, `on` and `equals`. Each of these keywords has a corresponding equivalent method or methods in LINQ although it can sometimes be a little more complicated as we shall see.

So, let us look at an example and see how it can be expressed using dot notation and query syntax4). For an example, let us look at a simple projection of people to their last names.

public struct Person
{
    public Person(string first, string last, DateTimeOffset dateOfBirth) : this()
    {
        FirstName = first;
        LastName = last;
        DateOfBirth = dateOfBirth;
    }
    
    public string FirstName { get; private set; }
    public string LastName { get; private set; }
    public DateTimeOffset DateOfBirth { get; private set; }
}

public static class Data
{
    public static IEnumerable<Person> People = new[] {
        new Person("John", "Smith", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 34 )),
        new Person("Bill", "Smith", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 20 )),
        new Person("Sarah", "Allans", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 19 )),
        new Person("John", "Johnson", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 44 )),
        new Person("James", "Jones", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 78 )),
        new Person("Alex", "Jones", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 30 )),
        new Person("Mabel", "Thomas", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 29 )),
        new Person("Sarah", "Brown", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 23 )),
        new Person("Gareth", "Smythe", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 100 )),
        new Person("Gregory", "Drake", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 90 )),
        new Person("Michael", "Johnson", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 56 )),
        new Person("Alex", "Smith", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 22 )),
        new Person("William", "Pickwick", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 17 )),
        new Person("Marcy", "Dickens", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 18 )),
        new Person("Erica", "Waters", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 26 ))
    };
}
Data.People.Select(p => p.LastName);
from person in Data.People
select person.LastName;

These two queries do the exact same thing, but I find that the dot notation wins out because it takes less typing and it looks clearer. However, if we decide we want to only get the ones that were born before 1980, things look a little more even.

Data.People
    .Where(p => p.DateOfBirth.Year < 1980)
    .Select(p => p.LastName)
from person in Data.People
where person.DateOfBirth.Year < 1980
select person.LastName;

Here, there is not much difference between them, so I'd probably leave this to personal preference5. However, as soon as we want a distinct list, the dot notation starts to win out again because C# does not contain a `distinct` keyword (though VB.NET does).

Mixing dot notation and query syntax in a single query can look messy, as shown here:

(from person in Data.People
where person.DateOfBirth.Year < 1980
select person.LastName).Distinct()

So, I prefer to settle on just one style of LINQ declaration for any particular query, or to use intermediate variables and separate the query into parts (this is especially useful on complex queries as it also provides clarity; being terse is cool, but it is unnecessary, and a great way to get people to hate you and your code).

Data.People
    .Where(p => p.DateOfBirth.Year < 1980)
    .Select(p => p.LastName)
    .Distinct();
var lastNames = from person in Data.People
                where person.DateOfBirth.Year < 1980
                select person.LastName;
lastNames.Distinct();

The `Distinct()` method is not the only LINQ method that has no query syntax alternative, there are plenty of others like `Aggregate()`, `Except()`, or `Range()`. This often means dot notation wins out or is at least part of a query written in query syntax. So, thus far, dot notation seems to have the advantage in the battle against query syntax. It is starting to look like some of my colleagues are right, query syntax sucks. Even if we use ordering or grouping, dot notation seems to be our friend or at least is no more painful than query syntax:

Data.People
    .OrderBy (p => p.LastName)
    .ThenBy (p => p.FirstName)
    .GroupBy(p=>p.DateOfBirth.Year);
from person in Data.People
orderby person.LastName,
        person.FirstName
group person by person.DateOfBirth.Year;

However, it is not always so easy. What if we want to introduce variables, group something other than the original object, or use more than one source collection? It is in these scenarios where query syntax irons a lot more of the complexity. Let's assume we have another collection containing newsletters that we need to send out to all our people. To generate the individual mailings, we would need to combine these two collections6.

Data.People.SelectMany(
    person => newsletters,
    (person, newsletter) => new {person,newsletter} );
from person in Data.People
from newsletter in newsletters
select new {person, newsletter};

I know which one is clearer to read and easier to remember when I need to write a similar query. The dot notation example makes me think for a minute what it is doing; projecting each person to the newsletters collection and, using `SelectMany()`, flattening the list then selecting one result per person/newsletter combination. Our query syntax example is doing the same thing, but I don't need to think too hard to see that. Query syntax is starting to look useful.

If we were to throw in some mid-query variables (useful to avoid calculating something multiple times or to improve clarity), or join collections, query syntax becomes really useful. What if each newsletter is on a different topic and we only want to send newsletters to people who are interested in that topic?

people.SelectMany(
    person => person.Value,
    ( person, topic ) => new
    {
        person,
        topic
    } ).Join(
        newsletters,
        t => t.topic,
        newsletter => newsletter.Value,
        ( t, newsletter ) => new
        {
            t.person,
            newsletter
        } );
from person in Data.People
from topic in person.Topics
join newsletter in newsletters on topic equals newsletter.Topic
select new {person, newsletter};

I know for sure I would need to go look up how to do that in dot notation7. Query syntax is an easier way to write more complex queries like this and provided that you understand your query chain, you can declare clear, performant queries.

 

In conclusion…

In this post I have attempted to show how both dot notation and query syntax (aka fluent notation) have their vices and their virtues, and in turn, armed you with the knowledge to choose wisely.

So, think about whether someone can read and maintain what you have written. Break down complex queries into parts. Consider moving some things to lazily evaluated methods. Understand what you are writing; if you look at it and have to think about why it works, it probably needs reworking. Always favour clarity and simplicity over dogma and cleverness; to draw inspiration from Jurassic Park, even though you could, stop to think whether you should.

LINQ is a complex feature of C# and .NET (and all the other .NET languages) and there are many things I have not covered. So, if you have any questions, please leave a comment. If I can't answer it, I will hopefully be able to direct you to someone who can. Alternatively, check out Edulinq by the inimitable Jon Skeet, head over to StackOverflow where there is an Internet of people waiting to help (including Jon Skeet), or get binging (googling, yahooing, altavistaring, whatever…)8.

And that brings us to the end of this series on LINQ. From deferred execution and the query chain to dot notation versus query syntax, I hope that I have managed to paint a favourable picture of LINQ, and helped to clear up some of the prejudices and confusions that surround it. LINQ is a powerful weapon in the arsenal of a .NET programmer; to not use it, would be a waste.

  1. Just the thought of the nested method calls or high number of placeholder variables makes me shudder []
  2. I guess LIQ was too suggestive for Microsoft []
  3. That statement is an absolute, Obi Sith Kenobi []
  4. I am definitely leaving the nested static methods approach to you as an exercise (in futility []
  5. Though if you changed the `person` variable to `p`, there is less typing in the query syntax , if that is a metric you are concerned with []
  6. Yes, a nested `foreach` can achieve this simple example, but this is just illustrative, and I'd argue cleaner than a `foreach` approach []
  7. That's why I cheated and wrote it in query syntax, then used Resharper to change it to dot notation for me []
  8. Back in my day, it was called searching…grumble grumble []

LINQ: Deferred Execution

This is part of a short series on the basics of LINQ:

In the first rant post of this short series on LINQ, I explained the motivation behind writing this series in the first place, which can be summarised as:

People don't know LINQ and that impacts my ability to make use of it; I should try to fix that.

To start, I'm going to explain what I believe is the most important concept in LINQ; deferred execution.

So, what is deferred execution?

Deferred execution code is not executed until the result is needed; the execution is put off (deferred) until later. By doing this we can combine a series of actions without actually executing any of them, then execute them at the time we need a result. This allows us to limit the execution of computationally expensive operations until we absolutely need them.

That's my description, here is one from an MSDN tutorial on LINQ-to-XML that perhaps puts it more clearly:

Deferred execution means that the evaluation of an expression is delayed until its realized value is actually required. Deferred execution can greatly improve performance when you have to manipulate large data collections, especially in programs that contain a series of chained queries or manipulations. In the best case, deferred execution enables only a single iteration through the source collection.

Some may be surprised to know that deferred execution was not new when LINQ arrived, it had already been around for quite some time in the form of iterator methods. In fact, it is iterator methods that give LINQ its deferred execution. Before we look at an iterator method, let's look at an example of immediate execution. For this example, we will give ourselves the task of taking a collection of people and outputting a collection of unique last names for all those born before 1980.

public struct Person
{
    public Person(string first, string last, DateTimeOffset dateOfBirth) : this()
    {
        FirstName = first;
        LastName = last;
        DateOfBirth = dateOfBirth;
    }
	
    public string FirstName { get; private set; }
    public string LastName { get; private set; }
    public DateTimeOffset DateOfBirth { get; private set; }
}

public static class Data
{
    public static IEnumerable<Person> People = new[] {
        new Person("John", "Smith", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 34 )),
        new Person("Bill", "Smith", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 20 )),
        new Person("Sarah", "Allans", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 19 )),
        new Person("John", "Johnson", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 44 )),
        new Person("James", "Jones", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 78 )),
        new Person("Alex", "Jones", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 30 )),
        new Person("Mabel", "Thomas", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 29 )),
        new Person("Sarah", "Brown", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 23 )),
        new Person("Gareth", "Smythe", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 100 )),
        new Person("Gregory", "Drake", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 90 )),
        new Person("Michael", "Johnson", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 56 )),
        new Person("Alex", "Smith", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 22 )),
        new Person("William", "Pickwick", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 17 )),
        new Person("Marcy", "Dickens", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 18 )),
        new Person("Erica", "Waters", DateTimeOffset.Now - TimeSpan.FromDays( 365 * 26 ))
    };
}
IEnumerable<string> GetLastNamesImmediate()
{
    var distinctNamesToReturn = new List<string>();
    foreach (var person in Data.People)
    {
        if (person.DateOfBirth.Year < 1980 && !distinctNamesToReturn.Contains(person.LastName))
        {
            distinctNamesToReturn.Add(person.LastName);
        }
    }
    return distinctNamesToReturn;
}

When this method is called, it iterates over the entire collection and then returns its result, which also can then be iterated. If the collection of people were huge and we only cared about the first five names, this would be incredibly slow. To turn this into deferred execution, we can write it like this:

IEnumerable<string> GetLastNamesLazyDeferredWithoutLINQ()
{
    var lastNamesWeHaveSeen = new List<string>();
    foreach (var person in Data.People)
    {
        if (person.DateOfBirth.Year < 1980 && !lastNamesWeHaveSeen.Contains(person.LastName))
        {
            lastNamesWeHaveSeen.Add(person.LastName);
            yield return person.LastName;
        }
    }
}

Now, none of the code in this method is executed until the first time something calls `MoveNext()` on the returned enumerable. This means we could take the first five names without processing the entire collection of people, giving us a potentially enormous performance gain. If each item in the generated collection were computationally expensive to produce, without lazy evaluation, that expense would be multiplied by the total number of items in the collection on every call to the generator method; however, with lazy evaluation, the consumer of the collection gets to decide how many items are computed and therefore, how much work gets done. This ability to defer and control computationally expensive operations is the power of deferred execution.

However, not every deferred action necessarily has low overhead. Deferred execution actually comes in two flavours; eager evaluation and lazy evaluation (the example above is an example of lazy evaluation)1. Every action in a deferred execution chain uses either lazy or eager evaluation. Though lazy evaluation is preferred, sometimes it is not possible to evaluate one item at a time, such as when sorting. Eagerly evaluated deferred execution allows us to at least defer the effort until we want it done.

An eagerly evaluated version of the iterator method we have been looking at might look like this:

IEnumerable<string> GetLastNamesEagerDeferredWithoutLINQ()
{
    var distinctNamesToReturn = new List<string>();
    foreach (var person in Data.People)
    {
        if (person.DateOfBirth.Year < 1980 && !distinctNamesToReturn.Contains(person.LastName))
        {
            distinctNamesToReturn.Add(person.LastName);
        }
    }
	
    foreach (var name in distinctNamesToReturn)
    {
        yield return name;
    }
}

In this example, because it is still an iterator method (it returns its results using `yield return`), none of the code is executed until the very first time the `MoveNext()` method is called on the returned enumerable, and therefore, the execution is deferred. When `MoveNext()` gets called for the first time, the entire collection of data is processed at once and then the results are output one by one as needed. The difference between this and the immediate execution equivalent we first looked at is that in this version, no work is done until a result is demanded.

Allowing the consumer of a collection to control how much work is done rather than work being dictated by the collection generator allows us to manage data more efficiently by building chains of operations and then processing the result in one go when needed. Lazy evaluation gives us the additional ability to spread the effort across each call to `MoveNext()`. The key to writing good LINQ is understanding which actions are immediate, which are deferred and lazily evaluated, which are deferred and eagerly evaluated, and why it matters. We will take a look at that next time.

  1. Quite often, people use 'deferred execution' and 'lazy evaluation' interchangeably, but they are not actually synonymous, nor is 'immediate execution' synonymous with 'eager evaluation'. []