I'm trying to connect cassandra database through python using cassandra driver .And it went successful with out any problem . When i tried to fetch the values from cassandra ,it has some formatted output like Row(values) .
python version 3.6
package : cassandra
from cassandra.cluster import Cluster
cluster = Cluster()
session = cluster.connect('employee')
k=session.execute("select count(*) from users")
print(k[0])
Output :
Row(count=11)
Expected :
11
From documentation:
By default, each row in the result set will be a named tuple. Each row will have a matching attribute for each column defined in the schema, such as name, age, and so on. You can also treat them as normal tuples by unpacking them or accessing fields by position.
So you can access your data by name as k[0].count, or by position as rows[0][0]
Please read Getting started document from driver's documentation - it will answer most of your questions.
Cassandra reply everything using something called row factory, which by default is a named tuple.
In your case, to access the output you should access k[0].count.
Related
I have been working on a project to import the Danish 2.5Million ATM transaction data set to derive some visualizations.
The data is hosted on a mysql server provided by the university. The objective is to import the data using Sqoop and then apply a few transformations to it using pyspark.
Link to the dataset here : https://www.kaggle.com/sparnord/danish-atm-transactions
The Sql server, that hosts this information has a few rows which are intentionally or unintentionally mangled.
Example:
So I have a very basic sqoop command which gets the details from the source database. However I run into an issue where there are values which have a double-quote " especially in the column message_text
Sqoop Command :
sqoop import --connect jdbc:mysql:{source-connection-string} --table SRC_ATM_TRANS --username {username}--password {password} --target-dir /user/root/etl_project --fields-terminated-by '|' --lines-terminated-by "\n" -m 1
Here is sample row that is imported in the transaction.
2017|January|1|Sunday|21|Active|85|Diebold Nixdorf|København|Regnbuepladsen|5|1550|55.676|12.571|DKK|MasterCard|4531|Withdrawal|4017|"Suspected malfunction|0.000|55.676|13|2618425|0.000|277|1010|93|3|280.000|0|75|803|Clouds
However the expected output should be
2017|January|1|Sunday|21|Active|85|Diebold Nixdorf|København|Regnbuepladsen|5|1550|55.676|12.571|DKK|MasterCard|4531|Withdrawal|4017|"Suspected malfunction,0.000|55.676|13|2618425|0.000|277|1010|93|3|280.000|0|75|803|Clouds|Cloudy
At first I was okay with this hoping that pyspark would handle the mangled data since the delimiters are specified.
But now I run into issues when populating my dataframe.
transactions = spark.read.option("sep","|").csv("/user/root/etl_project/part-m-00000", header = False,schema = transaction_schema)
However when I inspect my rows I see that the mangled data has caused the dataframe to put these affected values into a single column!
transactions.filter(transactions.message_code == "4017").collect()
Row(year=2017, month=u'January', day=1, weekday=u'Sunday', hour=17, atm_status=u'Active', atm_id=u'35', atm_manufacturer=u'NCR', atm_location=u'Aabybro', atm_streetname=u'\xc3\u0192\xcb\u0153stergade', atm_street_number=6, atm_zipcode=9440, atm_lat=57.162, atm_lon=9.73, currency=u'DKK', card_type=u'MasterCard', transaction_amount=7387, service=u'Withdrawal', message_code=u'4017', message_text=u'Suspected malfunction|0.000|57.158|10|2625037|0.000|276|1021|83|4|319.000|0|0|800|Clear', weather_lat=None, weather_lon=None, weather_city_id=None, weather_city_name=None, temp=None, pressure=None, humidity=None, wind_speed=None, wind_deg=None, rain_3h=None, clouds_all=None, weather_id=None, weather_main=None, weather_description=None)
At this point I am not sure on what to do?
Do I go ahead and create temporary columns to manage this and use a regex replacement to fill in these values ?
Or is there any better way I can import the data and manage these mangled values either in sqoop or in pyspark ?
I am using pymongo 3.11.3 in my notebook project. The value for int32 is returning constant 100 by pymongo. Query is returning fine in MongoDB IDE. Here is the code
client = MongoClient("localhost", 27017)
db = client['mongodb_vs_mysql']
mongo_result = db.collection['covid19'].find().sort("Cases_person", -1).limit(30);
for i in list(mongo_result):
print(i)
The database has different values but when querying with pymongo, it is showing 100 for that column.
Need help
I'd be fairly certain that you're looking at different databases; for a start, you have different (albeit similar) ids, and different field names (Daily_Cumulative vs Daily / cumulative)
I was doing mistake in this code
db.collection['covid19'] should be db['covid19']
I have a small log dataframe which has metadata regarding the ETL performed within a given notebook, the notebook is part of a bigger ETL pipeline managed in Azure DataFactory.
Unfortunately, it seems that Databricks cannot invoke stored procedures so I'm manually appending a row with the correct data to my log table.
however, I cannot figure out the correct sytnax to update a table given a set of conditions :
the statement I use to append a single row is as follows :
spark_log.write.jdbc(sql_url, 'internal.Job',mode='append')
this works swimmingly however, as my Data Factory is invoking a stored procedure,
I need to work in a query like
query = f"""
UPDATE [internal].[Job] SET
[MaxIngestionDate] date {date}
, [DataLakeMetadataRaw] varchar(MAX) NULL
, [DataLakeMetadataCurated] varchar(MAX) NULL
WHERE [IsRunning] = 1
AND [FinishDateTime] IS NULL"""
Is this possible ? if so can someone show me how?
Looking at the documentation this only seems to mention using select statements with the query parameter :
Target Database is an Azure SQL Database.
https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html
just to add this is a tiny operation, so performance is a non-issue.
You can't do single record updates using jdbc in Spark with dataframes. You can only append or replace the entire table.
You can do updates using pyodbc- requires installing the MSSQL ODBC driver (How to install PYODBC in Databricks) or you can use jdbc via JayDeBeApi (https://pypi.org/project/JayDeBeApi/)
I'm trying to access the types of columns in a table in redshift using psycopg2.
I'm doing this by running a simple query on pg_table_def like as follows:
SELECT * FROM pg_table_def;
This returns the traceback:
psycopg2.NotSupportedError: Column "schemaname" has unsupported type "name"
So it seems like the types of the columns that store schema (and other similar information on further queries) are not supported by psycopg2.
Has anyone run into this issue or a similar one and is aware of a workaround? My primary goal in this is to be able to return the types of columns in the table. For the purposes of what I'm doing, I can't use another postgresql adapter.
Using:
python- 3.6.2
psycopg2- 2.7.4
pandas- 0.17.1
You could do something like below, and could return the result back to calling service.
cur.execute("select * from pg_table_def where tablename='sales'")
results = cur.fetchall()
for row in results:
print ("ColumnNanme=>"+row[2] +",DataType=>"+row[3]+",encoding=>"+row[4])
Not sure about exception, if all the permissions are fine, then, it should work fine, print something like below.
ColumnNanme=>salesid,DataType=>integer,encoding=>lzo
ColumnNanme=>commission,DataType=>numeric(8,2),encoding=>lzo
ColumnNanme=>saledate,DataType=>date,encoding=>lzo
ColumnNanme=>description,DataType=>character varying(255),encoding=>lzo
I am using PySpark to perform SparkSQL on my Hive tables.
records = sqlContext.sql("SELECT * FROM my_table")
which retrieves the contents of the table.
When I use the filter argument as a string, it works okay:
records.filter("field_i = 3")
However, when I try to use the filter method, as documented here
records.filter(records.field_i == 3)
I am encountering this error
py4j.protocol.Py4JJavaError: An error occurred while calling o19.filter.
: org.apache.spark.sql.AnalysisException: resolved attributes field_i missing from field_1,field_2,...,field_i,...field_n
eventhough this field_i column clearly exists in the DataFrame object.
I prefer to use the second way because I need to use Python functions to perform record and field manipulations.
I am using Spark 1.3.0 in Cloudera Quickstart CDH-5.4.0 and Python 2.6.
From Spark DataFrame documentation
In Python it’s possible to access a DataFrame’s columns either by attribute (df.age) or by indexing (df['age']). While the former is convenient for interactive data exploration, users are highly encouraged to use the latter form, which is future proof and won’t break with column names that are also attributes on the DataFrame class.
It seems that the name of your field can be a reserved word, try with:
records.filter(records['field_i'] == 3)
What I did was to upgrade my Spark from 1.3.0 to 1.4.0 in Cloudera Quick Start CDH-5.4.0 and the second filtering feature works. Although I still can't explain why 1.3.0 has problems on that.