I'm trying to query for two different values from the same field or column. In this instance I would like to retrieve rows where the fulfilled item are true or false. Below is an example of what I'm trying to do.
const params = {
TableName: "Orders",
IndexName: 'fulfilled-shippingId-index',
KeyConditionExpression: "fulfilled = :fulfilled",
//FilterExpression : 'contains(fulfilled=true) OR fulfilled=false',
ExpressionAttributeValues: {
":fulfilled": "true",
":fulfilled": "false"
}
let me know if this isn't possible or if there would be a different way to do this through a loop or maybe just multiple requests from the application? As of now it just returns the last Expression Attribute Value.
thanks!
Unfortunately, this isn't possible.
KeyConditionExpression
The condition must perform an equality test on a single partition key
value.
You must specify a single partition key value.
Using a FilterExpression here won't help since FilterExpressions are applied after the data is read. Also, a FilterExpression cannot contain partition key or sort key attributes.
Since fulfilled = true OR fulfilled = false is a tautology, I would recommend just using Scan to read all of the items in your fulfilled-shippingId-index
Related
I have a db on Dynamodb. And writing some user scores to db. Also I have a lambda function which wrote it with nodejs. I want to get first 10 users who have most points. How could I scan this users?
Thanks a lot.
Max() in NoSQL is much trickier than in SQL. And it doesn't really scale - if you want very high scalability on achieving this let me know, but let's get back to the question.
Assuming your table looks like:
User
----------
userId - hashKey
score
...
Add a dummy category attribute to your table, which will be constant (for example value "A"). Create the index:
category - hash key
score - sort key
Query this index by hash key "A" in reserve order in order to get results much faster than a scan. But this scales to max 10GB (max partition size, all data being in same partition). Also make sure you project only needed attributes for this index, in order to save space.
You can go up to 30GB for example, by setting 3 categories ("A", "B", "C"), executing 3 queries and merge programatically the results. This will affect performance a bit, but still better than a full scan.
EDIT
var params = {
TableName: 'MyTableName',
Limit: 10,
// Set ScanIndexForward to false to display most recent entries first
ScanIndexForward: false,
KeyConditionExpression: 'category = : category',
ExpressionAttributeValues: {
':category': {
S: 'category',
},
},
};
dynamo.query(params, function(err, data) {
// handle data
});
source: https://www.debassociates.com/blog/query-dynamodb-table-from-a-lambda-function-with-nodejs-and-apex-up/
My goal is to do a sum of the messages_sent and emails_sent per each DISTINCT provider_id value for a given time range (fromDate < stats_date_id < toDate), but without specifying a provider_id. In other words, I need to know about any and all Providers within the specified time range and to sum their messages_sent and emails_sent.
I have a Cassandra table using an express-cassandra schema (in Node.js) as follows:
module.exports = {
fields: {
stats_provider_id: {
type: 'uuid',
default: {
'$db_function': 'uuid()'
}
},
stats_date_id: {
type: 'timeuuid',
default: {
'$db_function': 'now()'
}
},
provider_id: 'uuid',
provider_name: 'text',
messages_sent: 'int',
emails_sent: 'int'
},
key: [
[
'stats_date_id'
],
'created_at'
],
table_name: 'stats_provider',
options: {
timestamps: {
createdAt: 'created_at', // defaults to createdAt
updatedAt: 'updated_at' // defaults to updatedAt
}
}
}
To get it working, I was hoping it'd be as simple as doing the following:
let query = {
stats_date_id: {
'$gt': db.models.minTimeuuid(fromDate),
'$lt': db.models.maxTimeuuid(toDate)
}
};
let selectQueries = [
'provider_name',
'provider_id',
'count(direct_sent) as direct_sent',
'count(messages_sent) as messages_sent',
'count(emails_sent) as emails_sent',
];
// Query stats_provider table
let providerData = await db.models.instance.StatsProvider.findAsync(query, {select: selectQueries});
This, however, complains about needing to filter the results:
Error during find query on DB -> ResponseError: Cannot execute this query as it might involve data filtering and thus may have unpredictable performance.
I'm guessing you can't have a primary key and do date range searches on it? If so, what is the correct approach to this sort of query?
So while not having used Express-Cassandra, I can tell you that running a range query on your partition key is a hard "no." The reason for this, is that Cassandra can't determine a single node for that query, so it has to poll every node. As that's essentially a full scan of your table across multiple nodes, it throws that error to prevent you from running a bad query.
However, you can run a range query on a clustering key, provided that you are filtering on all of the keys prior to it. In your case, if I'm reading this right, your PRIMARY KEY looks like:
PRIMARY KEY (stats_date_id, created_at)
That primary key definition is going to be problematic for two reasons:
stats_date_id is a TimeUUID. This is great for data distribution. But it sucks for query flexibility. In fact, you will need to provide that exact TimeUUID value to return data for a specific partition. As a TimeUUID has millisecond precision, you'll need to know the exact time to query down to the millisecond. Maybe you have the ability to do that, but usually that doesn't make for a good partition key.
Any rows underneath that partition (created_at) will have to share that exact time, which usually leads to a lot of 1:1 cardinality ratios for partition:clustering keys.
My advice on fixing this, is to partition on a date column that has a slightly lower level of cardinality. Think about how many provider messages are usually saved within a certain timeframe. Also pick something that won't store too many provider messages together, as you don't want unbound partition growth (Cassandra has a hard limit of 2 billion cells per partition).
Maybe something like: PRIMARY KEY (week,created_at)
So then your CQL queries could look something like:
SELECT * FROM stats_provider
WHERE week='201909w1'
AND created_at > '20190901'
AND created_at < '20190905';
TL;DR;
Partition on a time bucket not quite as precise as something down to the ms, yet large enough to satisfy your usual query.
Apply the range filter on the first clustering key, within a partition.
I have one table TestTable and partition Key TestColumn.
Inputs Dates:
from_date= "2017-04-20T16:31:54.451071+00:00"
to_date = "2018-04-20T16:31:54.451071+00:00"
when I use equal query the date then it is working.
key_expr = Key('TestColumn').eq(to_date)
query_resp = table.query(KeyConditionExpression=key_expr)
but when I use between query then is not working.
key_expr = Key('TestColumn').between(from_date, to_date)
query_resp = table.query(KeyConditionExpression=key_expr)
Error:
Unknown err_msg while querying dynamodb: An error occurred (ValidationException) when calling the Query operation: Query key condition not supported
https://docs.aws.amazon.com/amazondynamodb/latest/APIReference/API_Query.html
DynamoDB Query will return data from one and only one partition, meaning you have to supply a single partition key in the request.
KeyConditionExpression
The condition that specifies the key value(s)
for items to be retrieved by the Query action.
The condition must perform an equality test on a single partition key
value.
You can optionally use a BETWEEN operator on a sort key (but you still have to supply a single partition key).
If you use a Scan you can use an ExpressionFilter and use the BETWEEN operator on TestColumn
I am trying to query Dynamo for modified_time > 1 day and jstatus = Error
Here, JobStatusIndex is the Global Secondary Index having, modified_time as the Partition Key and jstatus as the sort key.
dynamo.query({
TableName: "Jobs",
IndexName: "JobStatusIndex", // Global Secondry Index
KeyConditionExpression: `modified_time >= :ter and jstatus = :ste`,
ExpressionAttributeValues: {
':ter': moment().subtract(1, 'day').unix(),
':ste': "Error"
}
},(err) => console.log(err))
But I get an error that says:
ValidationException: Query key condition not supported
What could be the reason for this? I just don't get this.
I have gone through some SO questions, but it didn't solve the problem. I already have the required keys in place. What am I missing?
You can only use an equals operator on your partition key (modified_time). And to do a query, you must specify a single partition key.
You have a lot of different options depending on what you are trying to achieve. Most likely is you have your partition key and sort key the wrong way around for your GSI. If you make jstatus your partition key and modified_time your sort key you can do the query.
Alternatively you could consider doing a scan, which does not require you to specify any index or keys.
I've got a MongoDB database of metadata for about 300,000 photos. Each has a native unique ID that needs to be unique to protect against duplication insertions. It also has a time stamp.
I frequently need to run aggregate queries to see how many photos I have for each day, so I also have a date field in the format YYYY-MM-DD. This is obviously not unique.
Right now I only have an index on the id property, like so (using the Node driver):
collection.ensureIndex(
{ id:1 },
{ unique:true, dropDups: true },
function(err, indexName) { /* etc etc */ }
);
The group query for getting the photos by date takes quite a long time, as one can imagine:
collection.group(
{ date: 1 },
{},
{ count: 0 },
function ( curr, result ) {
result.count++;
},
function(err, grouped) { /* etc etc */ }
);
I've read through the indexing strategy, and I think I need to also index the date property. But I don't want to make it unique, of course (though I suppose it's fine to make it unique in combine with the unique id). Should I do a regular compound index, or can I chain the .ensureIndex() function and only specify uniqueness for the id field?
MongoDB does not have "mixed" type indexes which can be partially unique. On the other hand why don't you use _id instead of your id field if possible. It's already indexed and unique by definition so it will prevent you from inserting duplicates.
Mongo can only use a single index in a query clause - important to consider when creating indexes. For this particular query and requirements I would suggest to have a separate unique index on id field which you would get if you use _id. Additionally, you can create a non-unique index on date field only. If you run query like this:
db.collection.find({"date": "01/02/2013"}).count();
Mongo will be able to use index only to answer the query (covered index query) which is the best performance you can get.
Note that Mongo won't be able to use compound index on (id, date) if you are searching by date only. You query has to match index prefix first, i.e. if you search by id then (id, date) index can be used.
Another option is to pre aggregate in the schema itself. Whenever you insert a photo you can increment this counter. This way you don't need to run any aggregation jobs. You can also run some tests to determine if this approach is more performant than aggregation.