Melakukan repartition pada data

Sebuah dataframe text_df tersedia, dengan kolom id, word, dan chapter. Lima baris pertama text_df dicetak ke konsol.

Anda dapat menentukan bahwa ada 12 bab dengan perintah berikut:

text_df.select('chapter')\
       .distinct()\
       .sort('chapter')\
       .show(truncate=False)

Hasil perintah ini dicetak ke konsol sebagai Table 1.

Dataframe text_df saat ini berada dalam satu partisi. Misalkan Anda mengetahui bahwa langkah pemrosesan berikutnya akan mengelompokkan data berdasarkan bab. Pemrosesan data akan paling efisien jika setiap bab berada pada satu mesin. Untuk menghindari pengacakan data yang tidak perlu dari satu mesin ke mesin lain, mari lakukan repartition pada dataframe menjadi satu partisi per bab, menggunakan perintah repartition dan getNumPartitions yang diajarkan pada video pelajaran pertama di bab ini.

Jangan ragu untuk merujuk ke slide yang tersedia di sebelah kanan konsol jika Anda lupa bagaimana sesuatu dilakukan di video.

Latihan ini adalah bagian dari kursus

Pengantar Spark SQL dalam Python

Petunjuk latihan

Lakukan repartition pada text_df menjadi 12 partisi, dengan setiap bab berada pada partisinya masing-masing.
Tampilkan jumlah partisi pada dataframe yang baru.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Repartition text_df into 12 partitions on 'chapter' column
repart_df = text_df.____(____, ____)

# Prove that repart_df has 12 partitions
repart_df.____.____

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pengantar Spark SQL dalam Python

SkillTag.level.advancedSkillTag.label

4.7+

Mulai Kursus Gratis

In this chapter you will learn how to create and query a SQL table in Spark. Spark SQL brings the expressiveness of SQL to Spark. You will also learn how to use SQL window functions in Spark. Window functions perform a calculation across rows that are related to the current row. They greatly simplify achieving results that are difficult to express using only joins and traditional aggregations. We'll use window functions to perform running sums, running differences, and other operations that are challenging to perform in basic SQL.

Exercise 1: Creating and querying a SQL table in Spark Exercise 2: Create a SQL table from a dataframe Exercise 3: Determine the column names of a table Exercise 4: Window function SQL Exercise 5: Running sums using window function SQL Exercise 6: Fix the broken query Exercise 7: Dot notation and SQL Exercise 8: Aggregation, step by step Exercise 9: Aggregating the same column twice Exercise 10: Aggregate dot SQL Exercise 11: Convert window function from dot notation to SQL

In this chapter, you will be loading natural language text. Then you will apply a moving window analysis to find frequent word sequences.

Exercise 1: Memuat teks bahasa alami Exercise 2: Memuat dataframe dari berkas parquet Exercise 3: Membagi dan meledakkan kolom teks Exercise 4: Menggunakan monotonically_increasing_id()Exercise 5: Analisis jendela bergerak Exercise 6: Membuat data fitur jendela konteks Exercise 7: Melakukan repartition pada data

Latihan Saat Ini

Exercise 8: Urutan kata umum Exercise 9: Jenis data apakah ini Exercise 10: Menemukan urutan kata yang umum Exercise 11: 5-tuple unik dalam urutan terurut Exercise 12: 3-tuple tersering per bab

In the previous chapters you learned how to use the expressiveness of window function SQL. However, this expressiveness now makes it important that you understand how to properly cache dataframes and cache SQL tables. It is also important to know how to evaluate your application. You learn how to do do this using the Spark UI. You'll also learn a best practice for logging in Spark. Spark SQL brings with it another useful tool for tuning query performance issues, the query execution plan. You will learn how to use the execution plan for evaluating the provenance of a dataframe.

Exercise 1: Caching Exercise 2: Practice query plans 2 Exercise 3: Practicing caching: part 1 Exercise 4: Practicing caching: the SQL Exercise 5: Practicing caching: putting it all together Exercise 6: Caching and uncaching tables Exercise 7: The Spark UI Exercise 8: Spark UI storage tab Exercise 9: Inspecting cache in the Spark UI Exercise 10: Logging Exercise 11: Practice logging Exercise 12: Practice logging 2 Exercise 13: Query plans Exercise 14: Practice query plans Exercise 15: Practice reading query plans 2

Previous chapters provided you with the tools for loading raw text, tokenizing it, and extracting word sequences. This is already very useful for analysis, but it is also useful for machine learning. What you've learned now comes together by using logistic regression to classify text. By the conclusion of this chapter, you will have loaded raw natural language text data and used it to train a text classifier.

Exercise 1: Extract Transform Select Exercise 2: Practicing creating a UDF Exercise 3: Practicing array column Exercise 4: Creating feature data for classification Exercise 5: Creating a UDF for vector data Exercise 6: Applying a UDF to vector data Exercise 7: Transforming text to vector format Exercise 8: Text Classification Exercise 9: Label the data Exercise 10: Split the data Exercise 11: Train the classifier Exercise 12: Predicting and evaluating Exercise 13: Evaluate the classifier Exercise 14: Predict test data Exercise 15: Recap