# 3.1. Design Documents

In this section we’ll show how to write design documents, using the built-in JavaScript Query Server.

But before we start to write our first document, let’s take a look at the list of common objects that will be used during our code journey - we’ll be using them extensively within each function:

- Database information object
- Request object
- Response object
- UserCtx object
- Database Security object
- Guide to JavaScript Query Server

## 3.1.1. View Functions

Views are the primary tool used for querying and reporting on CouchDB databases.

### 3.1.1.1. Map Functions

`mapfun`(doc)

  Arguments: | doc – The document that is being processed

Map functions accept a single document as the argument and (optionally) `emit()` key/value pairs that are stored in a view.

    function (doc) {
      if (doc.type === 'post' && doc.tags && Array.isArray(doc.tags)) {
        doc.tags.forEach(function (tag) {
          emit(tag.toLowerCase(), 1);
        });
      }
    }

In this example a key/value pair is emitted for each value in the tags array of a document with a type of “post”. Note that `emit()` may be called many times for a single document, so the same document may be available by several different keys.

Also keep in mind that each document is sealed to prevent the situation where one map function changes document state and another receives a modified version.

For efficiency reasons, documents are passed to a group of map functions - each document is processed by a group of map functions from all views of the related design document. This means that if you trigger an index update for one view in the design document, all others will get updated too.

Since version 1.1.0, map supports CommonJS modules and the `require()` function.

### 3.1.1.2. Reduce and Rereduce Functions

`redfun`(keys, values[, rereduce])

  Arguments: | keys – Array of pairs of docid-key for related map function results. Always `null` if rereduce is running (has `true` value). values – Array of map function result values. rereduce – Boolean flag to indicate a rereduce run.
  Returns: | Reduces values

Reduce functions take two required arguments of keys and values lists - the result of the related map function - and an optional third value which indicates if rereduce mode is active or not. Rereduce is used for additional reduce values list, so when it is `true` there is no information about related keys (first argument is `null`).

Note that if the result of a reduce function is longer than the initial values list then a Query Server error will be raised. However, this behavior can be disabled by setting `reduce_limit` config option to `false`:

    [query_server_config]
    reduce_limit = false

While disabling `reduce_limit` might be useful for debug proposes, remember that the main task of reduce functions is to reduce the mapped result, not to make it bigger. Generally, your reduce function should converge rapidly to a single value - which could be an array or similar object.

#### 3.1.1.2.1. Built-in Reduce Functions

Additionally, CouchDB has a set of built-in reduce functions. These are implemented in Erlang and run inside CouchDB, so they are much faster than the equivalent JavaScript functions.

`_approx_count_distinct`

New in version 2.2.

Aproximates the number of distinct keys in a view index using a variant of the HyperLogLog algorithm. This algorithm enables an efficient, parallelizable computation of cardinality using fixed memory resources. CouchDB has configured the underlying data structure to have a relative error of ~2%.

As this reducer ignores the emitted values entirely, an invocation with `group=true` will simply return a value of 1 for every distinct key in the view. In the case of array keys, querying the view with a `group_level` specified will return the number of distinct keys that share the common group prefix in each row. The algorithm is also cognizant of the `startkey` and `endkey` boundaries and will return the number of distinct keys within the specified key range.

A final note regarding Unicode collation: this reduce function uses the binary representation of each key in the index directly as input to the HyperLogLog filter. As such, it will (incorrectly) consider keys that are not byte identical but that compare equal according to the Unicode collation rules to be distinct keys, and thus has the potential to overestimate the cardinality of the key space if a large number of such keys exist.

`_count`

Counts the number of values in the index with a given key. This could be implemented in JavaScript as:

    // could be replaced by _count
    function(keys, values, rereduce) {
        if (rereduce) {
            return sum(values);
        } else {
            return values.length;
        }
    }

`_stats`

Computes the following quantities for numeric values associated with each key: `sum`, `min`, `max`, `count`, and `sumsqr`. The behavior of the `_stats` function varies depending on the output of the map function. The simplest case is when the map phase emits a single numeric value for each key. In this case the `_stats` function is equivalent to the following JavaScript:

    // could be replaced by _stats
    function(keys, values, rereduce) {
        if (rereduce) {
            return {
                'sum': values.reduce(function(a, b) { return a + b.sum }, 0),
                'min': values.reduce(function(a, b) { return Math.min(a, b.min) }, Infinity),
                'max': values.reduce(function(a, b) { return Math.max(a, b.max) }, -Infinity),
                'count': values.reduce(function(a, b) { return a + b.count }, 0),
                'sumsqr': values.reduce(function(a, b) { return a + b.sumsqr }, 0)
            }
        } else {
            return {
                'sum': sum(values),
                'min': Math.min.apply(null, values),
                'max': Math.max.apply(null, values),
                'count': values.length,
                'sumsqr': (function() {
                var sumsqr = 0;

                values.forEach(function (value) {
                    sumsqr += value * value;
                });

                return sumsqr;
                })(),
            }
        }
    }

The `_stats` function will also work with “pre-aggregated” values from a map phase. A map function that emits an object containing `sum`, `min`, `max`, `count`, and `sumsqr` keys and numeric values for each can use the `_stats` function to combine these results with the data from other documents. The emitted object may contain other keys (these are ignored by the reducer), and it is also possible to mix raw numeric values and pre-aggregated objects in a single view and obtain the correct aggregated statistics.

Finally, `_stats` can operate on key-value pairs where each value is an array comprised of numbers or pre-aggregated objects. In this case every value emitted from the map function must be an array, and the arrays must all be the same length, as `_stats` will compute the statistical quantities above independently for each element in the array. Users who want to compute statistics on multiple values from a single document should either `emit` each value into the index separately, or compute the statistics for the set of values using the JavaScript example above and emit a pre-aggregated object.

`_sum`

In its simplest variation, `_sum` sums the numeric values associated with each key, as in the following JavaScript:

    // could be replaced by _sum
    function(keys, values) {
        return sum(values);
    }

As with `_stats`, the `_sum` function offers a number of extended capabilities. The `_sum` function requires that map values be numbers, arrays of numbers, or objects. When presented with array output from a map function, `_sum` will compute the sum for every element of the array. A bare numeric value will be treated as an array with a single element, and arrays with fewer elements will be treated as if they contained zeroes for every additional element in the longest emitted array. As an example, consider the following map output:

    {"total_rows":5, "offset":0, "rows": [
        {"id":"id1", "key":"abc", "value": 2},
        {"id":"id2", "key":"abc", "value": [3,5,7]},
        {"id":"id2", "key":"def", "value": [0,0,0,42]},
        {"id":"id2", "key":"ghi", "value": 1},
        {"id":"id1", "key":"ghi", "value": 3}
    ]}

The `_sum` for this output without any grouping would be:

    {"rows": [
        {"key":null, "value": [9,5,7,42]}
    ]}

while the grouped output would be

    {"rows": [
        {"key":"abc", "value": [5,5,7]},
        {"key":"def", "value": [0,0,0,42]},
        {"key":"ghi", "value": 4
    ]}

This is in contrast to the behavior of the `_stats` function which requires that all emitted values be arrays of identical length if any array is emitted.

It is also possible to have `_sum` recursively descend through an emitted object and compute the sums for every field in the object. Objects cannot be mixed with other data structures. Objects can be arbitrarily nested, provided that the values for all fields are themselves numbers, arrays of numbers, or objects.

Note

Why don’t reduce functions support CommonJS modules?

While map functions have limited access to stored modules through `require()`, there is no such feature for reduce functions. The reason lies deep inside the way map and reduce functions are processed by the Query Server. Let’s take a look at map functions first:

1. CouchDB sends all map functions in a processed design document to the Query Server.
2. the Query Server handles them one by one, compiles and puts them onto an internal stack.
3. after all map functions have been processed, CouchDB will send the remaining documents for indexing, one by one.
4. the Query Server receives the document object and applies it to every function from the stack. The emitted results are then joined into a single array and sent back to CouchDB.

Now let’s see how reduce functions are handled:

1. CouchDB sends as a single command the list of available reduce functions with the result list of key-value pairs that were previously returned from the map functions.
2. the Query Server compiles the reduce functions and applies them to the key-value lists. The reduced result is sent back to CouchDB.

As you may note, reduce functions are applied in a single shot to the map results while map functions are applied to documents one by one. This means that it’s possible for map functions to precompile CommonJS libraries and use them during the entire view processing, but for reduce functions they would be compiled again and again for each view result reduction, which would lead to performance degradation.

## 3.1.2. Show Functions

`showfun`(doc, req)

  Arguments: | doc – The document that is being processed; may be omitted. req – Request object.
  Returns: | Response object
  Return type: | object or string

Show functions are used to represent documents in various formats, commonly as HTML pages with nice formatting. They can also be used to run server-side functions without requiring a pre-existing document.

Basic example of show function could be:

    function(doc, req){
        if (doc) {
            return "Hello from " + doc._id + "!";
        } else {
            return "Hello, world!";
        }
    }

Also, there is more simple way to return json encoded data:

    function(doc, req){
        return {
            'json': {
                'id': doc['_id'],
                'rev': doc['_rev']
            }
        }
    }

and even files (this one is CouchDB logo):

    function(doc, req){
        return {
            'headers': {
                'Content-Type' : 'image/png',
            },
            'base64': ''.concat(
                'iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAMAAAAoLQ9TAAAAsV',
                'BMVEUAAAD////////////////////////5ur3rEBn////////////////wDBL/',
                'AADuBAe9EB3IEBz/7+//X1/qBQn2AgP/f3/ilpzsDxfpChDtDhXeCA76AQH/v7',
                '/84eLyWV/uc3bJPEf/Dw/uw8bRWmP1h4zxSlD6YGHuQ0f6g4XyQkXvCA36MDH6',
                'wMH/z8/yAwX64ODeh47BHiv/Ly/20dLQLTj98PDXWmP/Pz//39/wGyJ7Iy9JAA',
                'AADHRSTlMAbw8vf08/bz+Pv19jK/W3AAAAg0lEQVR4Xp3LRQ4DQRBD0QqTm4Y5',
                'zMxw/4OleiJlHeUtv2X6RbNO1Uqj9g0RMCuQO0vBIg4vMFeOpCWIWmDOw82fZx',
                'vaND1c8OG4vrdOqD8YwgpDYDxRgkSm5rwu0nQVBJuMg++pLXZyr5jnc1BaH4GT',
                'LvEliY253nA3pVhQqdPt0f/erJkMGMB8xucAAAAASUVORK5CYII=')
        }
    }

But what if you need to represent data in different formats via a single function? Functions `registerType()` and `provides()` are your the best friends in that question:

    function(doc, req){
        provides('json', function(){
            return {'json': doc}
        });
        provides('html', function(){
            return '<pre>' + toJSON(doc) + '</pre>'
        })
        provides('xml', function(){
            return {
                'headers': {'Content-Type': 'application/xml'},
                'body' : ''.concat(
                    '<?xml version="1.0" encoding="utf-8"?>\n',
                    '<doc>',
                    (function(){
                        escape = function(s){
                            return s.replace(/&quot;/g, '"')
                                    .replace(/&gt;/g, '>')
                                    .replace(/&lt;/g, '<')
                                    .replace(/&amp;/g, '&');
                        };
                        var content = '';
                        for(var key in doc){
                            if(!doc.hasOwnProperty(key)) continue;
                            var value = escape(toJSON(doc[key]));
                            var key = escape(key);
                            content += ''.concat(
                                '<' + key + '>',
                                value
                                '</' + key + '>'
                            )
                        }
                        return content;
                    })(),
                    '</doc>'
                )
            }
        })
        registerType('text-json', 'text/json')
        provides('text-json', function(){
            return toJSON(doc);
        })
    }

This function may return html, json , xml or our custom text json format representation of same document object with same processing rules. Probably, the xml provider in our function needs more care to handle nested objects correctly, and keys with invalid characters, but you’ve got the idea!

See also

CouchDB Guide:
  - Show Functions

## 3.1.3. List Functions

`listfun`(head, req)

  Arguments: | head – View Head Information req – Request object.
  Returns: | Last chunk.
  Return type: | string

While Show Functions are used to customize document presentation, List Functions are used for the same purpose, but on View Functions results.

The following list function formats the view and represents it as a very simple HTML page:

    function(head, req){
        start({
            'headers': {
                'Content-Type': 'text/html'
            }
        });
        send('<html><body><table>');
        send('<tr><th>ID</th><th>Key</th><th>Value</th></tr>');
        while(row = getRow()){
            send(''.concat(
                '<tr>',
                '<td>' + toJSON(row.id) + '</td>',
                '<td>' + toJSON(row.key) + '</td>',
                '<td>' + toJSON(row.value) + '</td>',
                '</tr>'
            ));
        }
        send('</table></body></html>');
    }

Templates and styles could obviously be used to present data in a nicer fashion, but this is an excellent starting point. Note that you may also use `registerType()` and `provides()` functions in a similar way as for Show Functions! However, note that `provides()` expects the return value to be a string when used inside a list function, so you’ll need to use `start()` to set any custom headers and stringify your JSON before returning it.

See also

CouchDB Guide:
  - Transforming Views with List Functions

## 3.1.4. Update Functions

`updatefun`(doc, req)

  Arguments: | doc – The document that is being processed. req – Request object
  Returns: | Two-element array: the first element is the (updated or new) document, which is committed to the database. If the first element is `null` no document will be committed to the database. If you are updating an existing document, it should already have an `_id` set, and if you are creating a new document, make sure to set its `_id` to something, either generated based on the input or the `req.uuid` provided. The second element is the response that will be sent back to the caller.

Update handlers are functions that clients can request to invoke server-side logic that will create or update a document. This feature allows a range of use cases such as providing a server-side last modified timestamp, updating individual fields in a document without first getting the latest revision, etc.

When the request to an update handler includes a document ID in the URL, the server will provide the function with the most recent version of that document. You can provide any other values needed by the update handler function via the `POST`/`PUT` entity body or query string parameters of the request.

A basic example that demonstrates all use-cases of update handlers:

    function(doc, req){
        if (!doc){
            if ('id' in req && req['id']){
                // create new document
                return [{'_id': req['id']}, 'New World']
            }
            // change nothing in database
            return [null, 'Empty World']
        }
        doc['world'] = 'hello';
        doc['edited_by'] = req['userCtx']['name']
        return [doc, 'Edited World!']
    }

## 3.1.5. Filter Functions

`filterfun`(doc, req)

  Arguments: | doc – The document that is being processed req – Request object
  Returns: | Boolean value: `true` means that doc passes the filter rules, `false` means that it does not.

Filter functions mostly act like Show Functions and List Functions: they format, or filter the changes feed.

### 3.1.5.1. Classic Filters

By default the changes feed emits all database documents changes. But if you’re waiting for some special changes, processing all documents is inefficient.

Filters are special design document functions that allow the changes feed to emit only specific documents that pass filter rules.

Let’s assume that our database is a mailbox and we need to handle only new mail events (documents with the status new). Our filter function would look like this:

    function(doc, req){
        // we need only `mail` documents
        if (doc.type != 'mail'){
            return false;
        }
        // we're interested only in `new` ones
        if (doc.status != 'new'){
            return false;
        }
        return true; // passed!
    }

Filter functions must return `true` if a document passed all the rules. Now, if you apply this function to the changes feed it will emit only changes about “new mails”:

    GET /somedatabase/_changes?filter=mailbox/new_mail HTTP/1.1

    {"results":[
    {"seq":"1-g1AAAAF9eJzLYWBg4MhgTmHgz8tPSTV0MDQy1zMAQsMcoARTIkOS_P___7MymBMZc4EC7MmJKSmJqWaYynEakaQAJJPsoaYwgE1JM0o1TjQ3T2HgLM1LSU3LzEtNwa3fAaQ_HqQ_kQG3qgSQqnoCqvJYgCRDA5ACKpxPWOUCiMr9hFUegKi8T1jlA4hKkDuzAC2yZRo","id":"df8eca9da37dade42ee4d7aa3401f1dd","changes":[{"rev":"1-c2e0085a21d34fa1cecb6dc26a4ae657"}]},
    {"seq":"9-g1AAAAIreJyVkEsKwjAURUMrqCOXoCuQ5MU0OrI70XyppcaRY92J7kR3ojupaSPUUgqWwAu85By4t0AITbJYo5k7aUNSAnyJ_SGFf4gEkvOyLPMsFtHRL8ZKaC1M0v3eq5ALP-X2a0G1xYKhgnONpmenjT04o_v5tOJ3LV5itTES_uP3FX9ppcAACaVsQAo38hNd_eVFt8ZklVljPqSPYLoH06PJhG0Cxq7-yhQcz-B4_fQCjFuqBjjewVF3E9cORoExSrpU_gHBTo5m","id":"df8eca9da37dade42ee4d7aa34024714","changes":[{"rev":"1-29d748a6e87b43db967fe338bcb08d74"}]},
    ],
    "last_seq":"10-g1AAAAIreJyVkEsKwjAURR9tQR25BF2B5GMaHdmdaNIk1FLjyLHuRHeiO9Gd1LQRaimFlsALvOQcuLcAgGkWKpjbs9I4wYSvkDu4cA-BALkoyzLPQhGc3GKSCqWEjrvfexVy6abc_SxQWwzRVHCuYHaxSpuj1aqfTyp-3-IlSrdakmH8oeKvrRSIkJhSNiKFjdyEm7uc6N6YTKo3iI_pw5se3vRsMiETE23WgzJ5x8s73n-9EMYNTUc4Pt5RdxPVDkYJYxR3qfwLwW6OZw"}

Note that the value of `last_seq` is 10-.., but we received only two records. Seems like any other changes were for documents that haven’t passed our filter.

We probably need to filter the changes feed of our mailbox by more than a single status value. We’re also interested in statuses like “spam” to update spam-filter heuristic rules, “outgoing” to let a mail daemon actually send mails, and so on. Creating a lot of similar functions that actually do similar work isn’t good idea - so we need a dynamic filter.

You may have noticed that filter functions take a second argument named request. This allows the creation of dynamic filters based on query parameters, user context and more.

The dynamic version of our filter looks like this:

    function(doc, req){
        // we need only `mail` documents
        if (doc.type != 'mail'){
            return false;
        }
        // we're interested only in requested status
        if (doc.status != req.query.status){
            return false;
        }
        return true; // passed!
    }

and now we have passed the status query parameter in the request to let our filter match only the required documents:

    GET /somedatabase/_changes?filter=mailbox/by_status&status=new HTTP/1.1

    {"results":[
    {"seq":"1-g1AAAAF9eJzLYWBg4MhgTmHgz8tPSTV0MDQy1zMAQsMcoARTIkOS_P___7MymBMZc4EC7MmJKSmJqWaYynEakaQAJJPsoaYwgE1JM0o1TjQ3T2HgLM1LSU3LzEtNwa3fAaQ_HqQ_kQG3qgSQqnoCqvJYgCRDA5ACKpxPWOUCiMr9hFUegKi8T1jlA4hKkDuzAC2yZRo","id":"df8eca9da37dade42ee4d7aa3401f1dd","changes":[{"rev":"1-c2e0085a21d34fa1cecb6dc26a4ae657"}]},
    {"seq":"9-g1AAAAIreJyVkEsKwjAURUMrqCOXoCuQ5MU0OrI70XyppcaRY92J7kR3ojupaSPUUgqWwAu85By4t0AITbJYo5k7aUNSAnyJ_SGFf4gEkvOyLPMsFtHRL8ZKaC1M0v3eq5ALP-X2a0G1xYKhgnONpmenjT04o_v5tOJ3LV5itTES_uP3FX9ppcAACaVsQAo38hNd_eVFt8ZklVljPqSPYLoH06PJhG0Cxq7-yhQcz-B4_fQCjFuqBjjewVF3E9cORoExSrpU_gHBTo5m","id":"df8eca9da37dade42ee4d7aa34024714","changes":[{"rev":"1-29d748a6e87b43db967fe338bcb08d74"}]},
    ],
    "last_seq":"10-g1AAAAIreJyVkEsKwjAURR9tQR25BF2B5GMaHdmdaNIk1FLjyLHuRHeiO9Gd1LQRaimFlsALvOQcuLcAgGkWKpjbs9I4wYSvkDu4cA-BALkoyzLPQhGc3GKSCqWEjrvfexVy6abc_SxQWwzRVHCuYHaxSpuj1aqfTyp-3-IlSrdakmH8oeKvrRSIkJhSNiKFjdyEm7uc6N6YTKo3iI_pw5se3vRsMiETE23WgzJ5x8s73n-9EMYNTUc4Pt5RdxPVDkYJYxR3qfwLwW6OZw"}

and we can easily change filter behavior with:

    GET /somedatabase/_changes?filter=mailbox/by_status&status=spam HTTP/1.1

    {"results":[
    {"seq":"6-g1AAAAIreJyVkM0JwjAYQD9bQT05gk4gaWIaPdlNNL_UUuPJs26im-gmuklMjVClFFoCXyDJe_BSAsA4jxVM7VHpJEswWyC_ktJfRBzEzDlX5DGPDv5gJLlSXKfN560KMfdTbL4W-FgM1oQzpmByskqbvdWqnc8qfvvHCyTXWuBu_K7iz38VCOOUENqjwg79hIvfvOhamQahROoVYn3-I5huwXSvm5BJsTbLTk3B8QiO58-_YMoMkT0cr-BwdRElmFKSNKniDcAcjmM","id":"8960e91220798fc9f9d29d24ed612e0d","changes":[{"rev":"3-cc6ff71af716ddc2ba114967025c0ee0"}]},
    ],
    "last_seq":"10-g1AAAAIreJyVkEsKwjAURR9tQR25BF2B5GMaHdmdaNIk1FLjyLHuRHeiO9Gd1LQRaimFlsALvOQcuLcAgGkWKpjbs9I4wYSvkDu4cA-BALkoyzLPQhGc3GKSCqWEjrvfexVy6abc_SxQWwzRVHCuYHaxSpuj1aqfTyp-3-IlSrdakmH8oeKvrRSIkJhSNiKFjdyEm7uc6N6YTKo3iI_pw5se3vRsMiETE23WgzJ5x8s73n-9EMYNTUc4Pt5RdxPVDkYJYxR3qfwLwW6OZw"}

Combining filters with a continuous feed allows creating powerful event-driven systems.

### 3.1.5.2. View Filters

View filters are the same as classic filters above, with one small difference: they use the map instead of the filter function of a view, to filter the changes feed. Each time a key-value pair is emitted from the map function, a change is returned. This allows avoiding filter functions that mostly do the same work as views.

To use them just pass filter=_view and view=designdoc/viewname as request parameters to the changes feed:

    GET /somedatabase/_changes?filter=_view&view=dname/viewname  HTTP/1.1

Note

Since view filters use map functions as filters, they can’t show any dynamic behavior since request object is not available.

See also

CouchDB Guide:
  - Guide to filter change notification

## 3.1.6. Validate Document Update Functions

`validatefun`(newDoc, oldDoc, userCtx, secObj)

  Arguments: | newDoc – New version of document that will be stored. oldDoc – Previous version of document that is already stored. userCtx – User Context Object secObj – Security Object
  Throws: | `forbidden` error to gracefully prevent document storing.
  Throws: | `unauthorized` error to prevent storage and allow the user to re-auth.

A design document may contain a function named validate_doc_update which can be used to prevent invalid or unauthorized document update requests from being stored. The function is passed the new document from the update request, the current document stored in the database, a User Context Object containing information about the user writing the document (if present), and a Security Object with lists of database security roles.

Validation functions typically examine the structure of the new document to ensure that required fields are present and to verify that the requesting user should be allowed to make changes to the document properties. For example, an application may require that a user must be authenticated in order to create a new document or that specific document fields be present when a document is updated. The validation function can abort the pending document write by throwing one of two error objects:

    // user is not authorized to make the change but may re-authenticate
    throw({ unauthorized: 'Error message here.' });

    // change is not allowed
    throw({ forbidden: 'Error message here.' });

Document validation is optional, and each design document in the database may have at most one validation function. When a write request is received for a given database, the validation function in each design document in that database is called in an unspecified order. If any of the validation functions throw an error, the write will not succeed.

Example: The `_design/_auth` ddoc from _users database uses a validation function to ensure that documents contain some required fields and are only modified by a user with the `_admin` role:

    function(newDoc, oldDoc, userCtx, secObj) {
        if (newDoc._deleted === true) {
            // allow deletes by admins and matching users
            // without checking the other fields
            if ((userCtx.roles.indexOf('_admin') !== -1) ||
                (userCtx.name == oldDoc.name)) {
                return;
            } else {
                throw({forbidden: 'Only admins may delete other user docs.'});
            }
        }

        if ((oldDoc && oldDoc.type !== 'user') || newDoc.type !== 'user') {
            throw({forbidden : 'doc.type must be user'});
        } // we only allow user docs for now

        if (!newDoc.name) {
            throw({forbidden: 'doc.name is required'});
        }

        if (!newDoc.roles) {
            throw({forbidden: 'doc.roles must exist'});
        }

        if (!isArray(newDoc.roles)) {
            throw({forbidden: 'doc.roles must be an array'});
        }

        if (newDoc._id !== ('org.couchdb.user:' + newDoc.name)) {
            throw({
                forbidden: 'Doc ID must be of the form org.couchdb.user:name'
            });
        }

        if (oldDoc) { // validate all updates
            if (oldDoc.name !== newDoc.name) {
                throw({forbidden: 'Usernames can not be changed.'});
            }
        }

        if (newDoc.password_sha && !newDoc.salt) {
            throw({
                forbidden: 'Users with password_sha must have a salt.' +
                    'See /_utils/script/couch.js for example code.'
            });
        }

        var is_server_or_database_admin = function(userCtx, secObj) {
            // see if the user is a server admin
            if(userCtx.roles.indexOf('_admin') !== -1) {
                return true; // a server admin
            }

            // see if the user a database admin specified by name
            if(secObj && secObj.admins && secObj.admins.names) {
                if(secObj.admins.names.indexOf(userCtx.name) !== -1) {
                    return true; // database admin
                }
            }

            // see if the user a database admin specified by role
            if(secObj && secObj.admins && secObj.admins.roles) {
                var db_roles = secObj.admins.roles;
                for(var idx = 0; idx < userCtx.roles.length; idx++) {
                    var user_role = userCtx.roles[idx];
                    if(db_roles.indexOf(user_role) !== -1) {
                        return true; // role matches!
                    }
                }
            }

            return false; // default to no admin
        }

        if (!is_server_or_database_admin(userCtx, secObj)) {
            if (oldDoc) { // validate non-admin updates
                if (userCtx.name !== newDoc.name) {
                    throw({
                        forbidden: 'You may only update your own user document.'
                    });
                }
                // validate role updates
                var oldRoles = oldDoc.roles.sort();
                var newRoles = newDoc.roles.sort();

                if (oldRoles.length !== newRoles.length) {
                    throw({forbidden: 'Only _admin may edit roles'});
                }

                for (var i = 0; i < oldRoles.length; i++) {
                    if (oldRoles[i] !== newRoles[i]) {
                        throw({forbidden: 'Only _admin may edit roles'});
                    }
                }
            } else if (newDoc.roles.length > 0) {
                throw({forbidden: 'Only _admin may set roles'});
            }
        }

        // no system roles in users db
        for (var i = 0; i < newDoc.roles.length; i++) {
            if (newDoc.roles[i][0] === '_') {
                throw({
                    forbidden:
                    'No system roles (starting with underscore) in users db.'
                });
            }
        }

        // no system names as names
        if (newDoc.name[0] === '_') {
            throw({forbidden: 'Username may not start with underscore.'});
        }

        var badUserNameChars = [':'];

        for (var i = 0; i < badUserNameChars.length; i++) {
            if (newDoc.name.indexOf(badUserNameChars[i]) >= 0) {
                throw({forbidden: 'Character `' + badUserNameChars[i] +
                        '` is not allowed in usernames.'});
            }
        }
    }

Note

The `return` statement is used only for function, it has no impact on the validation process.

See also

CouchDB Guide:
  - Validation Functions