26 - Amazon Timestream¶
Creating resources¶
[10]:
import awswrangler as wr
import pandas as pd
from datetime import datetime
wr.timestream.create_database("sampleDB")
wr.timestream.create_table("sampleDB", "sampleTable", memory_retention_hours=1, magnetic_retention_days=1)
Write¶
[11]:
df = pd.DataFrame(
{
"time": [datetime.now(), datetime.now(), datetime.now()],
"dim0": ["foo", "boo", "bar"],
"dim1": [1, 2, 3],
"measure": [1.0, 1.1, 1.2],
}
)
rejected_records = wr.timestream.write(
df=df,
database="sampleDB",
table="sampleTable",
time_col="time",
measure_col="measure",
dimensions_cols=["dim0", "dim1"],
)
print(f"Number of rejected records: {len(rejected_records)}")
Number of rejected records: 0
Query¶
[12]:
wr.timestream.query(
'SELECT time, measure_value::double, dim0, dim1 FROM "sampleDB"."sampleTable" ORDER BY time DESC LIMIT 3'
)
[12]:
time | measure_value::double | dim0 | dim1 | |
---|---|---|---|---|
0 | 2020-12-08 19:15:32.468 | 1.0 | foo | 1 |
1 | 2020-12-08 19:15:32.468 | 1.2 | bar | 3 |
2 | 2020-12-08 19:15:32.468 | 1.1 | boo | 2 |
Deleting resources¶
[13]:
wr.timestream.delete_table("sampleDB", "sampleTable")
wr.timestream.delete_database("sampleDB")